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diff --git a/v1.0.0/examples/TimeSeries.html b/v1.0.0/examples/TimeSeries.html index 896f8ca39..a8a7d9abb 100644 --- a/v1.0.0/examples/TimeSeries.html +++ b/v1.0.0/examples/TimeSeries.html @@ -1209,7 +1209,7 @@

TimeSeries: A Few Useful Features
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© Copyright 2021, salesforce.com, inc..

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+ + + + + \ No newline at end of file diff --git a/v2.0.2/index.html b/v2.0.2/index.html new file mode 100644 index 000000000..2f1600745 --- /dev/null +++ b/v2.0.2/index.html @@ -0,0 +1,344 @@ + + + + + + Welcome to Merlion’s documentation! — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

Welcome to Merlion’s documentation!

+

Merlion is a Python library for time series intelligence. It features a unified interface for many commonly used +models and datasets for forecasting, anomaly detection, and change +point detection on both univariate and multivariate time series, along with standard +pre-processing and post-processing layers. +It has several modules to improve ease-of-use, +including visualization, +anomaly score calibration to improve interpetability, +AutoML for hyperparameter tuning and model selection, +and model ensembling. +Merlion also provides a unique evaluation framework +that simulates the live deployment and re-training of a model in production. +This library aims to provide engineers and researchers a one-stop solution to rapidly develop models +for their specific time series needs, and benchmark them across multiple time series datasets.

+
+

Installation

+

Merlion consists of two sub-packages: merlion implements the library’s core time series intelligence features, +and ts_datasets provides standardized data loaders for multiple time series datasets. These loaders load +time series as pandas.DataFrame s with accompanying metadata.

+

You can install merlion from PyPI by calling pip install salesforce-merlion. You may install from source by +cloning the Merlion repo and calling pip install Merlion/, or +pip install -e Merlion/ to install in editable mode. You may install additional optional dependencies via +pip install salesforce-merlion[all], or by calling pip install "Merlion/[all]" if installing from source. +Individually, the optional dependencies include dashboard for a GUI dashboard, +spark for a distributed computation backend with PySpark, and deep-learning for all deep learning models.

+

To install the data loading package ts_datasets, clone the Merlion +repo and call pip install -e Merlion/ts_datasets/. This package must be +installed in editable mode (i.e. with the -e flag) if you don’t want to manually specify the root directory of +every dataset when initializing its data loader.

+

Note the following external dependencies:

+
    +

  1. Some of our forecasting models depend on OpenMP. Some of our forecasting models depend on OpenMP. +If using conda, please conda install -c conda-forge lightgbm +before installing our package. This will ensure that OpenMP is configured to work with the lightgbm package +(one of our dependencies) in your conda environment. +If using Mac, please install Homebrew and call brew install libomp so that the +OpenMP libary is available for the model. +This is relevant for the +LGBMForecaster, +which is also used as a part of the DefaultForecaster.

    2. +

  2. Some of our anomaly detection models depend on having the Java Development Kit (JDK) installed. For Ubuntu, call +sudo apt-get install openjdk-11-jdk. For Mac OS, install Homebrew and call +brew tap adoptopenjdk/openjdk && brew install --cask adoptopenjdk11. Also ensure that java can be found +on your PATH, and that the JAVA_HOME environment variable is set. +This is relevant for the RandomCutForest +which is also used as a part of the DefaultDetector.

    3. +
+
+
+

Getting Started

+

The easiest way to get started is to use the GUI web-based dashboard. +This dashboard provides a great way to quickly experiment with many models on your own custom datasets. +To use it, install Merlion with the optional dashboard dependency (i.e. +pip install salesforce-merlion[dashboard]), and call python -m merlion.dashboard from the command line. +You can view the dashboard at http://localhost:8050.

+

For code resources, we recommend the linked tutorials on anomaly detection +and forecasting. After that, you should read in more detail about Merlion’s +main data structure for representing time series here.

+
+

Contents:

+ +
+
+
+
+

Indices and tables

+ +
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+
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+ + Versions + v2.0.2 + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.dashboard.html b/v2.0.2/merlion.dashboard.html new file mode 100644 index 000000000..c51532c20 --- /dev/null +++ b/v2.0.2/merlion.dashboard.html @@ -0,0 +1,327 @@ + + + + + + merlion.dashboard package — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + +
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+

merlion.dashboard package

+

This package includes a GUI dashboard app for Merlion, providing a convenient way to train +and test a time series forecasting or anomaly detection model supported in Merlion. To launch +the dashboard app, type the following command: python -m merlion.dashboard.

+

It will launch a Dash app on http://localhost:8050/ by default. After opening the link, the app +will create a folder merlion in your home directory. This folder includes the datasets you want to +analyze or train a model with (in the data folder), and the trained models for time series +forecasting or anomaly detection (in the models folder).

+

The app has three tabs. The first one is called “file manager” in which you can upload your datasets +(the datasets will be stored in ~/merlion/data), check basic statistics of the datasets, visualize +the time series data, or download a particular trained model:

+_images/dashboard_file.png +

You can click “Drag & Drop” to upload the file to the merlion folder (our app is designed to support +docker deployment, so it doesn’t allow to open a local file directly). If you use the app on a local +machine, you can also copy the data to ~/merlion/data directly. The supported data file is in +the csv format, where the first column should be either integer Unix timestamps in milliseconds, or datetimes in a +string format (e.g., “1970-01-01 00:00:00”). The other columns are the features/variables.

+

Clicking the load button will load the dataset and show the time series figure on the right hand side. +It will also show some basic statistics, e.g., time series length, mean/std for each variable. +If you have already trained a model using the dashboard, you can select the model you want to download +and click the download button. The model and its configuration file will be compressed into a zip file.

+

The second tab is used to train a time series anomaly detection model:

+_images/dashboard_anomaly.png +

The app provides full support for these models, where you can choose different algorithms and set particular parameters +according to your needs. To train a model, you need to:

+
    +

  • Select the dataset: You can select a single training dataset if there is no test dataset, and then choose +a train/test split fraction for splitting this dataset into training and test dataset for evaluation. +If you have the test dataset, you can choose “Separate train/test files” and select the test dataset, +and then the model will be trained with the training dataset and evaluated with the separate test dataset. +The screenshot above uses a single data file, where we use the first 15% for training and the last 85% for testing.

    • +

  • Set the feature columns: Merlion supports both univariate and multivariate time series anomaly detection, +so you can choose one or more features on which to train an anomaly detection model.

    • +

  • Set the label column: If the dataset has a label column, you can set it for evaluation. Otherwise, +ignore this setting.

    • +

  • Select an anomaly detection algorithm: You need to choose an anomaly detection algorithm such as +IsolationForest. You may modify the model’s hyperparameters if the default values do not work well.

    • +

  • Set threshold parameters: You can also test different settings for the detection threshold to +determine which value is better for your specific application. Note that updating the threshold will +not re-train the entire model; it will simply change the post-processing applied by the trained model.

    • +
+

The training procedure begins after clicking the train button, and the trained model is saved in the +folder ~/merlion/models/algorithm_name. The figure on the right hand side shows the detection results +on the test dataset, and the tables show the training and testing performance metrics if you set the +label column.

+

The third tab is used to train a time series forecasting model supported in Merlion:

+_images/dashboard_forecast.png +

The app provides full support for these models, where you can choose different algorithms and set particular parameters +according to your needs. To train a model, you need to:

+
    +

  • Select the dataset: You can select a single training dataset if there is no test dataset, and then choose +a train/test split fraction for splitting this dataset into training and test dataset for evaluation. +If you have the test dataset, you can choose “Separate train/test files” and select the test dataset, +and then the model will be trained with the training dataset and evaluated with the separate test dataset. +The screenshot above uses separate train/test files.

    • +

  • Set the target column: You need to set the target column whose value you wish to forecast (required), +any additional features to use for multivariate forecasting (optional), +and the exogenous variables whose values are known a priori (optional).

    • +

  • Select a forecasting algorithm: Finally, you need to choose a forecasting algorithm such as +Arima, AutoETS. You may modify the model’s hyperparameters if the default values do not work well.

    • +
+

The training procedure begins after clicking the train button. It may take some time to finish model +training. After the model is trained, the model files will be saved in the folder ~/merlion/models/algorithm_name. +The figure on the right hand side shows the forecasting results on the test dataset, and the tables +show the training and testing performance metrics.

+
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.evaluate.html b/v2.0.2/merlion.evaluate.html new file mode 100644 index 000000000..a090eb664 --- /dev/null +++ b/v2.0.2/merlion.evaluate.html @@ -0,0 +1,1175 @@ + + + + + + merlion.evaluate package — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

merlion.evaluate package

+

This sub-package implements utilities and metrics for evaluating the performance +of time series models on different tasks.

+ ++++ + + + + + + + + + + + +

base

Base class for an automated model evaluation framework.

anomaly

Metrics and utilities for evaluating time series anomaly detection models.

forecast

Metrics and utilities for evaluating forecasting models in a continuous sense.

+
+

merlion.evaluate.base

+

Base class for an automated model evaluation framework.

+
+
+class merlion.evaluate.base.EvaluatorConfig(train_window=None, retrain_freq=None, cadence=None)
+

Bases: object

+

Abstract class which defines an evaluator config.

+
+
Parameters
+
    +

  • train_window (Optional[float]) – the maximum duration of data we would like to train the model on. None means no limit.

    • +

  • retrain_freq (Optional[float]) – the frequency at which we want to re-train the model. None means we only train the +model once on the initial training data.

    • +

  • cadence (Optional[float]) – the frequency at which we want to obtain predictions from the model. +None means that we obtain a new prediction at the same frequency as the model’s predictive horizon. +0 means that we obtain a new prediction at every timestamp.

    • +
+
+
+
+
+property train_window: Optional[Union[Timedelta, DateOffset]]
+
+
Returns
+

the maximum duration of data we would like to train the model on. None means no limit.

+
+
+
+ +
+
+property retrain_freq: Optional[Union[Timedelta, DateOffset]]
+
+
Returns
+

the frequency at which we want to re-train the model. None means we only train the model on the +initial training data.

+
+
+
+ +
+
+property cadence: Union[Timedelta, DateOffset]
+
+
Returns
+

the cadence at which we are having our model produce new predictions. Defaults to the retraining +frequency if not explicitly provided.

+
+
+
+ +
+
+property horizon: DateOffset
+
+
Returns
+

the horizon our model is predicting into the future. Equal to the prediction cadence by default.

+
+
+
+ +
+
+to_dict()
+
+ +
+ +
+
+class merlion.evaluate.base.EvaluatorBase(model, config)
+

Bases: object

+

An evaluator simulates the live deployment of a model on historical data. +It trains a model on an initial time series, and then re-trains that model +at a specified frequency.

+

The EvaluatorBase.get_predict method returns the train & test predictions +of a model, as if it were being trained incrementally on the test data in +the manner described above.

+

Subclasses define slightly different protocols for different tasks, e.g. +anomaly detection vs. forecasting.

+
+
Parameters
+
+
+
+
+
+config_class
+

alias of EvaluatorConfig

+
+ +
+
+property train_window
+
+ +
+
+property retrain_freq
+
+ +
+
+property cadence
+
+ +
+
+property horizon
+
+ +
+
+default_train_kwargs()
+
+
Return type
+

dict

+
+
+
+ +
+
+default_retrain_kwargs()
+
+
Return type
+

dict

+
+
+
+ +
+
+get_predict(train_vals, test_vals, exog_data=None, train_kwargs=None, retrain_kwargs=None)
+

Initialize the model by training it on an initial set of train data. +Get the model’s predictions on the test data, retraining the model as +appropriate.

+
+
Parameters
+
    +

  • train_vals (TimeSeries) – initial training data

    • +

  • test_vals (TimeSeries) – all data where we want to get the model’s predictions and compare it to the ground truth

    • +

  • exog_data (Optional[TimeSeries]) – any exogenous data (only used for some models)

    • +

  • train_kwargs (Optional[dict]) – dict of keyword arguments we want to use for the initial training process

    • +

  • retrain_kwargs (Optional[dict]) – dict of keyword arguments we want to use for all subsequent retrainings

    • +
+
+
Return type
+

Tuple[Any, Union[TimeSeries, List[TimeSeries]]]

+
+
Returns
+

(train_result, result). train_result is the output of training the model on train_vals +(None if pretrained is True). result is the model’s predictions on test_vals, and is +specific to each evaluation task.

+
+
+
+ +
+
+abstract evaluate(ground_truth, predict, metric)
+

Given the ground truth time series & the model’s prediction (as produced +by EvaluatorBase.get_predict), compute the specified evaluation +metric. If no metric is specified, return the appropriate score +accumulator for the task. Implementation is task-specific.

+
+ +
+ +
+
+

merlion.evaluate.anomaly

+

Metrics and utilities for evaluating time series anomaly detection models.

+
+
+class merlion.evaluate.anomaly.ScoreType(value)
+

Bases: Enum

+

The algorithm to use to compute true/false positives/negatives. See the technical report +for more details on each score type. Merlion’s preferred default is revised point-adjusted.

+
+
+Pointwise = 0
+
+ +
+
+PointAdjusted = 1
+
+ +
+
+RevisedPointAdjusted = 2
+
+ +
+ +
+
+class merlion.evaluate.anomaly.TSADScoreAccumulator(num_tp_anom=0, num_tp_pointwise=0, num_tp_point_adj=0, num_fn_anom=0, num_fn_pointwise=0, num_fn_point_adj=0, num_fp=0, num_tn=0, tp_score=0.0, fp_score=0.0, tp_detection_delays=None, tp_anom_durations=None, anom_durations=None)
+

Bases: object

+

Accumulator which maintains summary statistics describing an anomaly +detection algorithm’s performance. Can be used to compute many different +time series anomaly detection metrics.

+
+
+precision(score_type=ScoreType.RevisedPointAdjusted)
+
+ +
+
+recall(score_type=ScoreType.RevisedPointAdjusted)
+
+ +
+
+f1(score_type=ScoreType.RevisedPointAdjusted)
+
+ +
+
+f_beta(score_type=ScoreType.RevisedPointAdjusted, beta=1.0)
+
+ +
+
+mean_time_to_detect()
+
+ +
+
+mean_detected_anomaly_duration()
+
+ +
+
+mean_anomaly_duration()
+
+ +
+
+nab_score(tp_weight=1.0, fp_weight=0.11, fn_weight=1.0, tn_weight=0.0)
+

Computes the NAB score, given the accumulated performance metrics and +the specified weights for different types of errors. The score is +described in section II.C of https://arxiv.org/pdf/1510.03336.pdf. +At a high level, this score is a cost-sensitive, recency-weighted +accuracy measure for time series anomaly detection.

+

NAB uses the following profiles for benchmarking +(https://github.com/numenta/NAB/blob/master/config/profiles.json):

+
    +

  • standard (default) +- tp_weight = 1.0, fp_weight = 0.11, fn_weight = 1.0

    • +

  • reward low false positive rate +- tp_weight = 1.0, fp_weight = 0.22, fn_weight = 1.0

    • +

  • reward low false negative rate +- tp_weight = 1.0, fp_weight = 0.11, fn_weight = 2.0

    • +
+

Note that tn_weight is ignored.

+
+
Parameters
+
    +

  • tp_weight – relative weight of true positives.

    • +

  • fp_weight – relative weight of false positives.

    • +

  • fn_weight – relative weight of false negatives.

    • +

  • tn_weight – relative weight of true negatives. Ignored, but +included for completeness.

    • +
+
+
Returns
+

NAB score

+
+
+
+ +
+ +
+
+merlion.evaluate.anomaly.accumulate_tsad_score(ground_truth, predict, max_early_sec=None, max_delay_sec=None, metric=None)
+

Computes the components required to compute multiple different types of +performance metrics for time series anomaly detection.

+
+
Parameters
+
    +

  • ground_truth (Union[TimeSeries, UnivariateTimeSeries]) – A time series indicating whether each time step +corresponds to an anomaly.

    • +

  • predict (Union[TimeSeries, UnivariateTimeSeries]) – A time series with the anomaly score predicted for each +time step. Detections correspond to nonzero scores.

    • +

  • max_early_sec – The maximum amount of time (in seconds) the anomaly +detection is allowed to occur before the actual incidence. If None, no +early detections are allowed. Note that None is the same as 0.

    • +

  • max_delay_sec – The maximum amount of time (in seconds) the anomaly +detection is allowed to occur after the start of the actual incident +(but before the end of the actual incident). If None, we allow any +detection during the duration of the incident. Note that None differs +from 0 because 0 means that we only permit detections that are early +or exactly on time!

    • +

  • metric – A function which takes a TSADScoreAccumulator as input and +returns a float. The TSADScoreAccumulator object is returned if +metric is None.

    • +
+
+
Return type
+

Union[TSADScoreAccumulator, float]

+
+
+
+ +
+
+class merlion.evaluate.anomaly.TSADMetric(value)
+

Bases: Enum

+

Enumeration of evaluation metrics for time series anomaly detection. +For each value, the name is the metric, and the value is a partial +function of form f(ground_truth, predicted, **kwargs)

+
+
+MeanTimeToDetect = functools.partial(<function accumulate_tsad_score>, metric=<function TSADScoreAccumulator.mean_time_to_detect>)
+
+ +
+
+F1 = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.f1>, score_type=<ScoreType.RevisedPointAdjusted: 2>))
+
+ +
+
+Precision = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.precision>, score_type=<ScoreType.RevisedPointAdjusted: 2>))
+
+ +
+
+Recall = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.recall>, score_type=<ScoreType.RevisedPointAdjusted: 2>))
+
+ +
+
+PointwiseF1 = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.f1>, score_type=<ScoreType.Pointwise: 0>))
+
+ +
+
+PointwisePrecision = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.precision>, score_type=<ScoreType.Pointwise: 0>))
+
+ +
+
+PointwiseRecall = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.recall>, score_type=<ScoreType.Pointwise: 0>))
+
+ +
+
+PointAdjustedF1 = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.f1>, score_type=<ScoreType.PointAdjusted: 1>))
+
+ +
+
+PointAdjustedPrecision = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.precision>, score_type=<ScoreType.PointAdjusted: 1>))
+
+ +
+
+PointAdjustedRecall = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.recall>, score_type=<ScoreType.PointAdjusted: 1>))
+
+ +
+
+NABScore = functools.partial(<function accumulate_tsad_score>, metric=<function TSADScoreAccumulator.nab_score>)
+
+ +
+
+NABScoreLowFN = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.nab_score>, fn_weight=2.0))
+
+ +
+
+NABScoreLowFP = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.nab_score>, fp_weight=0.22))
+
+ +
+
+F2 = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.f_beta>, score_type=<ScoreType.RevisedPointAdjusted: 2>, beta=2.0))
+
+ +
+
+F5 = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.f_beta>, score_type=<ScoreType.RevisedPointAdjusted: 2>, beta=5.0))
+
+ +
+ +
+
+class merlion.evaluate.anomaly.TSADEvaluatorConfig(max_early_sec=None, max_delay_sec=None, **kwargs)
+

Bases: EvaluatorConfig

+

Configuration class for a TSADEvaluator.

+
+
Parameters
+
    +

  • max_early_sec (Optional[float]) – the maximum number of seconds we allow an anomaly +to be detected early.

    • +

  • max_delay_sec (Optional[float]) – if an anomaly is detected more than this many +seconds after its start, it is not counted as being detected.

    • +
+
+
+
+ +
+
+class merlion.evaluate.anomaly.TSADEvaluator(model, config)
+

Bases: EvaluatorBase

+

Simulates the live deployment of an anomaly detection model.

+
+
Parameters
+
    +

  • model – the model to evaluate.

    • +

  • config – the evaluation configuration.

    • +
+
+
+
+
+config_class
+

alias of TSADEvaluatorConfig

+
+ +
+
+property max_early_sec
+
+ +
+
+property max_delay_sec
+
+ +
+
+default_retrain_kwargs()
+
+
Return type
+

dict

+
+
+
+ +
+
+get_predict(train_vals, test_vals, exog_data=None, train_kwargs=None, retrain_kwargs=None, post_process=True)
+

Initialize the model by training it on an initial set of train data. +Simulate real-time anomaly detection by the model, while re-training it +at the desired frequency.

+
+
Parameters
+
    +

  • train_vals (TimeSeries) – initial training data

    • +

  • test_vals (TimeSeries) – all data where we want to get the model’s predictions +and compare it to the ground truth

    • +

  • exog_data (Optional[TimeSeries]) – any exogenous data (only used for some models)

    • +

  • train_kwargs (Optional[dict]) – dict of keyword arguments we want to use for the +initial training process. Typically, you will want to provide the +key “anomaly_labels” here, if you have training data with labeled +anomalies, as well as the key “post_rule_train_config”, if you want +to use a custom training config for the model’s post-rule.

    • +

  • retrain_kwargs (Optional[dict]) – dict of keyword arguments we want to use for all +subsequent retrainings. Typically, you will not supply any this +argument.

    • +

  • post_process – whether to apply the model’s post-rule on the +returned results.

    • +
+
+
Return type
+

Tuple[TimeSeries, TimeSeries]

+
+
Returns
+

(train_result, result). train_result is a TimeSeries +of the model’s anomaly scores on train_vals. result is a +TimeSeries of the model’s anomaly scores on test_vals.

+
+
+
+ +
+
+evaluate(ground_truth, predict, metric=None)
+
+
Parameters
+
    +

  • ground_truth (TimeSeries) – TimeSeries of ground truth anomaly labels

    • +

  • predict (TimeSeries) – TimeSeries of predicted anomaly scores

    • +

  • metric (Optional[TSADMetric]) – the TSADMetric we wish to evaluate.

    • +
+
+
Return type
+

Union[TSADScoreAccumulator, float]

+
+
Returns
+

the value of the evaluation metric, if one is given. A +TSADScoreAccumulator otherwise.

+
+
+
+ +
+ +
+
+

merlion.evaluate.forecast

+

Metrics and utilities for evaluating forecasting models in a continuous sense.

+
+
+class merlion.evaluate.forecast.ForecastScoreAccumulator(ground_truth, predict, insample=None, periodicity=1, ub=None, lb=None, target_seq_index=None)
+

Bases: object

+

Accumulator which maintains summary statistics describing a forecasting +algorithm’s performance. Can be used to compute many different forecasting metrics.

+
+
Parameters
+
    +

  • ground_truth (Union[UnivariateTimeSeries, TimeSeries]) – ground truth time series

    • +

  • predict (Union[UnivariateTimeSeries, TimeSeries]) – predicted truth time series

    • +

  • (optional) (target_seq_index) – time series used for training model. This value is used for computing MSES, MSIS

    • +

  • (optional) – periodicity. m=1 indicates the non-seasonal time series, +whereas m>1 indicates seasonal time series. This value is used for computing MSES, MSIS.

    • +

  • (optional) – upper bound of 95% prediction interval. This value is used for computing MSIS

    • +

  • (optional) – lower bound of 95% prediction interval. This value is used for computing MSIS

    • +

  • (optional) – the index of the target sequence, for multivariate.

    • +
+
+
+
+
+check_before_eval()
+
+ +
+
+mae()
+

Mean Absolute Error (MAE)

+

For ground truth time series \(y\) and predicted time series \(\hat{y}\) +of length \(T\), it is computed as

+
+\[\frac{1}{T}\sum_{t=1}^T{(|y_t - \hat{y}_t|)}.\]
+
+ +
+
+marre()
+

Mean Absolute Ranged Relative Error (MARRE)

+

For ground truth time series \(y\) and predicted time series \(\hat{y}\) +of length \(T\), it is computed as

+
+\[100 \cdot \frac{1}{T} \sum_{t=1}^{T} {\left| \frac{y_t - \hat{y}_t} {\max_t{y_t} - +\min_t{y_t}} \right|}.\]
+
+ +
+
+rmse()
+

Root Mean Squared Error (RMSE)

+

For ground truth time series \(y\) and predicted time series \(\hat{y}\) +of length \(T\), it is computed as

+
+\[\sqrt{\frac{1}{T}\sum_{t=1}^T{(y_t - \hat{y}_t)^2}}.\]
+
+ +
+
+smape()
+

symmetric Mean Absolute Percentage Error (sMAPE). For ground truth time series \(y\) +and predicted time series \(\hat{y}\) of length \(T\), it is computed as

+
+\[200 \cdot \frac{1}{T} +\sum_{t=1}^{T}{\frac{\left| y_t - \hat{y}_t \right|}{\left| y_t \right| ++ \left| \hat{y}_t \right|}}.\]
+
+ +
+
+rmspe()
+

Root Mean Squared Percent Error (RMSPE)

+

For ground truth time series \(y\) and predicted time series \(\hat{y}\) +of length \(T\), it is computed as

+
+\[100 \cdot \sqrt{\frac{1}{T}\sum_{t=1}^T\frac{(y_t - \hat{y}_t)}{y_t}^2}.\]
+
+ +
+
+mase()
+

Mean Absolute Scaled Error (MASE) +For ground truth time series \(y\) and predicted time series \(\hat{y}\) +of length \(T\). In sample time series \(\hat{x}\) of length \(N\) +and periodicity \(m\) it is computed as

+
+\[\frac{1}{T}\cdot\frac{\sum_{t=1}^{T}\left| y_t +- \hat{y}_t \right|}{\frac{1}{N-m}\sum_{t=m+1}^{N}\left| x_t - x_{t-m} \right|}.\]
+
+ +
+
+msis()
+

Mean Scaled Interval Score (MSIS) +This metric evaluates the quality of 95% prediction intervals. +For ground truth time series \(y\) and predicted time series \(\hat{y}\) +of length \(T\), the lower and upper bounds of the prediction intervals +\(L\) and \(U\). Given in sample time series \(\hat{x}\) of length \(N\) +and periodicity \(m\), it is computed as

+
+\[\frac{1}{T}\cdot\frac{\sum_{t=1}^{T} (U_t - L_t) + 100 \cdot (L_t - y_t)[y_t<L_t] ++ 100\cdot(y_t - U_t)[y_t > U_t]}{\frac{1}{N-m}\sum_{t=m+1}^{N}\left| x_t - x_{t-m} \right|}.\]
+
+ +
+ +
+
+merlion.evaluate.forecast.accumulate_forecast_score(ground_truth, predict, insample=None, periodicity=1, ub=None, lb=None, metric=None, target_seq_index=None)
+
+
Return type
+

Union[ForecastScoreAccumulator, float]

+
+
+
+ +
+
+class merlion.evaluate.forecast.ForecastMetric(value)
+

Bases: Enum

+

Enumeration of evaluation metrics for time series forecasting. For each value, +the name is the metric, and the value is a partial function of form +f(ground_truth, predict, **kwargs). Here, ground_truth is the +original time series, and predict is the result returned by a +ForecastEvaluator.

+
+
+MAE = functools.partial(<function accumulate_forecast_score>, metric=<function ForecastScoreAccumulator.mae>)
+

Mean Absolute Error (MAE) is formulated as:

+
+\[\frac{1}{T}\sum_{t=1}^T{(|y_t - \hat{y}_t|)}.\]
+
+ +
+
+MARRE = functools.partial(<function accumulate_forecast_score>, metric=<function ForecastScoreAccumulator.marre>)
+

Mean Absolute Ranged Relative Error (MARRE) is formulated as:

+
+\[100 \cdot \frac{1}{T} \sum_{t=1}^{T} {\left| \frac{y_t +- \hat{y}_t} {\max_t{y_t} - \min_t{y_t}} \right|}.\]
+
+ +
+
+RMSE = functools.partial(<function accumulate_forecast_score>, metric=<function ForecastScoreAccumulator.rmse>)
+

Root Mean Squared Error (RMSE) is formulated as:

+
+\[\sqrt{\frac{1}{T}\sum_{t=1}^T{(y_t - \hat{y}_t)^2}}.\]
+
+ +
+
+sMAPE = functools.partial(<function accumulate_forecast_score>, metric=<function ForecastScoreAccumulator.smape>)
+

symmetric Mean Absolute Percentage Error (sMAPE) is formulated as:

+
+\[200 \cdot \frac{1}{T}\sum_{t=1}^{T}{\frac{\left| y_t +- \hat{y}_t \right|}{\left| y_t \right| + \left| \hat{y}_t \right|}}.\]
+
+ +
+
+RMSPE = functools.partial(<function accumulate_forecast_score>, metric=<function ForecastScoreAccumulator.rmspe>)
+

Root Mean Square Percent Error is formulated as:

+
+\[100 \cdot \sqrt{\frac{1}{T}\sum_{t=1}^T\frac{(y_t - \hat{y}_t)}{y_t}^2}.\]
+
+ +
+
+MASE = functools.partial(<function accumulate_forecast_score>, metric=<function ForecastScoreAccumulator.mase>)
+

Mean Absolute Scaled Error (MASE) is formulated as:

+
+\[\frac{1}{T}\cdot\frac{\sum_{t=1}^{T}\left| y_t + - \hat{y}_t \right|}{\frac{1}{N-m}\sum_{t=m+1}^{N}\left| x_t - x_{t-m} \right|}.\]
+
+ +
+
+MSIS = functools.partial(<function accumulate_forecast_score>, metric=<function ForecastScoreAccumulator.msis>)
+

Mean Scaled Interval Score (MSIS) is formulated as:

+
+\[\frac{1}{T}\cdot\frac{\sum_{t=1}^{T} (U_t - L_t) + 100 \cdot (L_t - y_t)[y_t<L_t] + + 100\cdot(y_t - U_t)[y_t > U_t]}{\frac{1}{N-m}\sum_{t=m+1}^{N}\left| x_t - x_{t-m} \right|}.\]
+
+ +
+ +
+
+class merlion.evaluate.forecast.ForecastEvaluatorConfig(horizon=None, **kwargs)
+

Bases: EvaluatorConfig

+

Configuration class for a ForecastEvaluator

+
+
Parameters
+

horizon (Optional[float]) – the model’s prediction horizon. Whenever the model makes +a prediction, it will predict horizon seconds into the future.

+
+
+
+
+property horizon: Optional[Union[Timedelta, DateOffset]]
+
+
Returns
+

the horizon our model is predicting into the future. Defaults to the retraining frequency.

+
+
+
+ +
+
+property cadence: Optional[Union[Timedelta, DateOffset]]
+
+
Returns
+

the cadence at which we are having our model produce new predictions. Defaults to the predictive +horizon if there is one, and the retraining frequency otherwise.

+
+
+
+ +
+ +
+
+class merlion.evaluate.forecast.ForecastEvaluator(model, config)
+

Bases: EvaluatorBase

+

Simulates the live deployment of an forecaster model.

+
+
Parameters
+
    +

  • model – the model to evaluate.

    • +

  • config – the evaluation configuration.

    • +
+
+
+
+
+config_class
+

alias of ForecastEvaluatorConfig

+
+ +
+
+property horizon
+
+ +
+
+property cadence
+
+ +
+
+evaluate(ground_truth, predict, metric=ForecastMetric.sMAPE)
+
+
Parameters
+
+
+
+
+ +
+ +
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
+ +
+
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+ + + +
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+ + + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.html b/v2.0.2/merlion.html new file mode 100644 index 000000000..3f0e3773e --- /dev/null +++ b/v2.0.2/merlion.html @@ -0,0 +1,434 @@ + + + + + + merlion: Time Series Intelligence — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

merlion: Time Series Intelligence

+

merlion is a Python library for time series intelligence. We support the following key features, +each associated with its own sub-package:

+
    +

  • merlion.models: A library of models unified under a single shared interface, with specializations +for anomaly detection and forecasting. More specifically, we have

    + +
    • +

  • merlion.dashboard: A GUI dashboard app for Merlion, which can be started with +python -m merlion.dashboard. This dashboard provides a good way to quickly experiment many models on a new +time series.

    • +

  • merlion.spark: APIs to integrate Merlion with PySpark for using distributed computing to run training +and inference on multiple time series in parallel.

    • +

  • merlion.transform: Data pre-processing layer which implements many standard data transformations used in +time series analysis. Transforms are callable objects, and each model has its own configurable model.transform +which it uses to pre-process all input time series for both training and inference.

    • +

  • merlion.post_process: Post-processing rules to apply on the output of a model. Currently, these are +specific to anomaly detection, and include

    +
      +

    • merlion.post_process.calibrate: Rules to calibrate the anomaly scores returned by a model, to +be interpretable as z-scores, i.e. as standard deviations of a standard normal random variable. Each +anomaly detection model has a model.calibrator from this module, which can optionally be applied to ensure +that the model’s anomaly scores are calibrated.

      • +

    • merlion.post_process.threshold: Rules to reduce the noisiness of an anomaly detection model’s outputs. +Each anomaly detection model has a model.threshold from this module, which can optionally be applied to +filter the model’s predicted sequence of anomaly scores.

      • +
    +
    • +

  • merlion.evaluate: Evaluation metrics & pipelines to simulate the live deployment of a time series model +for any task.

    • +

  • merlion.plot: Automated visualization of model outputs for univariate time series

    • +

  • merlion.utils: Various utilities, including the TimeSeries class, resampling functions, +Bayesian conjugate priors, reconciliation for hierarchical time series, and more.

    • +
+

The key classes for input and output are merlion.utils.time_series.TimeSeries and +merlion.utils.time_series.UnivariateTimeSeries. Notably, these classes have transparent inter-operability +with pandas.DataFrame and pandas.Series, respectively. Check this tutorial +for some examples on how to use these classes, or the API docs linked above for a full list of features.

+

The full API documentation is outlined below:

+
+ +
+
+ +
+
+ +
+
+ +
+
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+
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+ + Versions + v2.0.2 + + +
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+ + + + +
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+ + + + +
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+ + + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.models.anomaly.change_point.html b/v2.0.2/merlion.models.anomaly.change_point.html new file mode 100644 index 000000000..ecc52117e --- /dev/null +++ b/v2.0.2/merlion.models.anomaly.change_point.html @@ -0,0 +1,622 @@ + + + + + + anomaly.change_point — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ + + +
+

anomaly.change_point

+

Contains all change point detection algorithms. These models implement the anomaly detector interface, but +they are specialized for detecting change points in time series.

+ ++++ + + + + + +

bocpd

Bayesian online change point detection algorithm.

+
+

anomaly.change_point.bocpd

+

Bayesian online change point detection algorithm.

+
+
+class merlion.models.anomaly.change_point.bocpd.ChangeKind(value)
+

Bases: Enum

+

Enum representing the kinds of changes points we would like to detect. +Enum values correspond to the Bayesian ConjPrior class used to detect each sort of change point.

+
+
+Auto = None
+

Automatically choose the Bayesian conjugate prior we would like to use.

+
+ +
+
+LevelShift = <class 'merlion.utils.conj_priors.MVNormInvWishart'>
+

Model data points with a normal distribution, to detect level shifts.

+
+ +
+
+TrendChange = <class 'merlion.utils.conj_priors.BayesianMVLinReg'>
+

Model data points as a linear function of time, to detect trend changes.

+
+ +
+ +
+
+class merlion.models.anomaly.change_point.bocpd.BOCPDConfig(change_kind=ChangeKind.Auto, cp_prior=0.01, lag=None, min_likelihood=1e-16, max_forecast_steps=None, target_seq_index=None, invert_transform=None, transform=None, enable_calibrator=False, max_score=1000, threshold=None, enable_threshold=True, **kwargs)
+

Bases: ForecasterConfig, NoCalibrationDetectorConfig

+

Config class for BOCPD (Bayesian Online Change Point Detection).

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • change_kind (Union[str, ChangeKind]) – the kind of change points we would like to detect

    • +

  • cp_prior – prior belief probability of how frequently changepoints occur

    • +

  • lag – the maximum amount of delay/lookback (in number of steps) allowed for detecting change points. +If lag is None, we will consider the entire history. Note: we do not recommend lag = 0.

    • +

  • min_likelihood – we will discard any hypotheses whose probability of being a change point is +lower than this threshold. Lower values improve accuracy at the cost of time and space complexity.

    • +

  • max_forecast_steps – the maximum number of steps the model is allowed to forecast. Ignored.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • enable_calibrator – False because this config assumes calibrated outputs from the model.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +
+
+
+
+
+property change_kind: ChangeKind
+
+ +
+ +
+
+class merlion.models.anomaly.change_point.bocpd.BOCPD(config=None)
+

Bases: ForecastingDetectorBase

+

Bayesian online change point detection algorithm described by +Adams & MacKay (2007). +At a high level, this algorithm models the observed data using Bayesian conjugate priors. If an observed value +deviates too much from the current posterior distribution, it is likely a change point, and we should start +modeling the time series from that point forwards with a freshly initialized Bayesian conjugate prior.

+

The get_anomaly_score() method returns a z-score corresponding to the probability of each point being +a change point. The forecast() method returns the predicted values (and standard error) of the underlying +piecewise model on the relevant data.

+
+
Parameters
+

config (Optional[BOCPDConfig]) – model configuration

+
+
+
+
+config_class
+

alias of BOCPDConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

All forecasters can work on multivariate data, since they only forecast a single target univariate.

+
+ +
+
+property last_train_time
+
+
Returns
+

the last time (as a pandas.Timestamp) that the model was trained on

+
+
+
+ +
+
+property n_seen
+
+
Returns
+

the number of data points seen so far

+
+
+
+ +
+
+property change_kind: ChangeKind
+
+
Returns
+

the kind of change points we would like to detect

+
+
+
+ +
+
+property cp_prior: float
+
+
Returns
+

prior belief probability of how frequently changepoints occur

+
+
+
+ +
+
+property lag: int
+
+
Returns
+

the maximum amount of delay allowed for detecting change points. A higher lag can increase +recall, but it may decrease precision.

+
+
+
+ +
+
+property min_likelihood: float
+
+
Returns
+

we will not consider any hypotheses (about whether a particular point is a change point) +with likelihood lower than this threshold

+
+
+
+ +
+
+train_pre_process(train_data, exog_data=None, return_exog=False)
+

Applies pre-processing steps common for training most models.

+
+
Parameters
+

train_data (TimeSeries) – the original time series of training data

+
+
Return type
+

Union[TimeSeries, Tuple[TimeSeries, Optional[TimeSeries]]]

+
+
Returns
+

the training data, after any necessary pre-processing has been applied

+
+
+
+ +
+
+update(time_series)
+

Updates the BOCPD model’s internal state using the time series values provided.

+
+
Parameters
+

time_series (TimeSeries) – time series whose values we are using to update the internal state of the model

+
+
Returns
+

anomaly score associated with each point (based on the probability of it being a change point)

+
+
+
+ +
+
+get_anomaly_score(time_series, time_series_prev=None, exog_data=None)
+

Returns the model’s predicted sequence of anomaly scores.

+
+
Parameters
+
    +

  • time_series (TimeSeries) – the TimeSeries we wish to predict anomaly scores +for.

    • +

  • time_series_prev (Optional[TimeSeries]) – a TimeSeries immediately preceding +time_series. If given, we use it to initialize the time series +anomaly detection model. Otherwise, we assume that time_series +immediately follows the training data.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

a univariate TimeSeries of anomaly scores

+
+
+
+ +
+
+get_figure(*, time_series=None, **kwargs)
+
+
Parameters
+
    +

  • time_series (Optional[TimeSeries]) – the time series over whose timestamps we wish to make a forecast. Exactly one of +time_series or time_stamps should be provided.

    • +

  • time_stamps – Either a list of timestamps we wish to forecast for, or the number of steps (int) +we wish to forecast for. Exactly one of time_series or time_stamps should be provided.

    • +

  • time_series_prev – a time series immediately preceding time_series. If given, we use it to initialize +the forecaster’s state. Otherwise, we assume that time_series immediately follows the training data.

    • +

  • exog_data – A time series of exogenous variables. Exogenous variables are known a priori, and they are +independent of the variable being forecasted. exog_data must include data for all of time_stamps; +if time_series_prev is given, it must include data for all of time_series_prev.time_stamps as well. +Optional. Only supported for models which inherit from ForecasterExogBase.

    • +

  • plot_anomaly – Whether to plot the model’s predicted anomaly scores.

    • +

  • filter_scores – whether to filter the anomaly scores by the post-rule before plotting them.

    • +

  • plot_forecast – Whether to plot the model’s forecasted values.

    • +

  • plot_forecast_uncertainty – whether to plot uncertainty estimates (the inter-quartile range) for forecast +values. Not supported for all models.

    • +

  • plot_time_series_prev – whether to plot time_series_prev (and the model’s fit for it). Only used if +time_series_prev is given.

    • +
+
+
Return type
+

Figure

+
+
Returns
+

a Figure of the model’s anomaly score predictions and/or forecast.

+
+
+
+ +
+ +
+
+ + +
+
+ +
+
+
+
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.models.anomaly.forecast_based.html b/v2.0.2/merlion.models.anomaly.forecast_based.html new file mode 100644 index 000000000..159a9c12c --- /dev/null +++ b/v2.0.2/merlion.models.anomaly.forecast_based.html @@ -0,0 +1,1088 @@ + + + + + + anomaly.forecast_based — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ + + +
+

anomaly.forecast_based

+

Contains all forecaster-based anomaly detectors. These models support all functionality +of both anomaly detectors (merlion.models.anomaly) and forecasters +(merlion.models.forecast).

+

Forecasting-based anomaly detectors are instances of an abstract ForecastingDetectorBase +class. Many forecasting models support anomaly detection variants, where the anomaly score +is based on the difference between the predicted and true time series value, and optionally +the model’s uncertainty in its own prediction.

+

Note that the model will detect anomalies in only one target univariate, though the underlying +forecaster may model the full multivariate time series to predict said univariate.

+ ++++ + + + + + + + + + + + + + + + + + + + + +

base

Base class for anomaly detectors based on forecasting models.

arima

Classic ARIMA (AutoRegressive Integrated Moving Average) forecasting model, adapted for anomaly detection.

sarima

Seasonal ARIMA (SARIMA) forecasting model, adapted for anomaly detection.

ets

ETS (error, trend, seasonal) forecasting model, adapted for anomaly detection.

prophet

Adaptation of Facebook's Prophet forecasting model to anomaly detection.

mses

MSES (Multi-Scale Exponential Smoother) forecasting model adapted for anomaly detection.

+
+

anomaly.forecast_based.base

+

Base class for anomaly detectors based on forecasting models.

+
+
+class merlion.models.anomaly.forecast_based.base.ForecastingDetectorBase(config)
+

Bases: ForecasterBase, DetectorBase

+

Base class for a forecast-based anomaly detector.

+
+
Parameters
+

config (ForecasterConfig) – model configuration

+
+
+
+
+forecast_to_anom_score(time_series, forecast, stderr)
+

Compare a model’s forecast to a ground truth time series, in order to compute anomaly scores. By default, we +compute a z-score if model uncertainty (stderr) is given, or the residuals if there is no model uncertainty.

+
+
Parameters
+
    +

  • time_series (TimeSeries) – the ground truth time series.

    • +

  • forecast (TimeSeries) – the model’s forecasted values for the time series

    • +

  • stderr (Optional[TimeSeries]) – the standard errors of the model’s forecast

    • +
+
+
Return type
+

DataFrame

+
+
Returns
+

Anomaly scores based on the difference between the ground truth values and the model’s forecast.

+
+
+
+ +
+
+train(train_data, train_config=None, exog_data=None, anomaly_labels=None, post_rule_train_config=None)
+

Trains the anomaly detector (unsupervised) and its post-rule (supervised, if labels are given) on train data.

+
+
Parameters
+
    +

  • train_data (TimeSeries) – a TimeSeries of metric values to train the model.

    • +

  • train_config – Additional training configs, if needed. Only required for some models.

    • +

  • anomaly_labels – a TimeSeries indicating which timestamps are anomalous. Optional.

    • +

  • post_rule_train_config – The config to use for training the model’s post-rule. The model’s default +post-rule train config is used if none is supplied here.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

A TimeSeries of the model’s anomaly scores on the training data.

+
+
+
+ +
+
+train_post_process(train_result, anomaly_labels=None, post_rule_train_config=None)
+

Converts the train result (anom scores on train data) into a TimeSeries object and trains the post-rule.

+
+
Parameters
+
    +

  • train_result (Tuple[Union[TimeSeries, DataFrame], Union[TimeSeries, DataFrame, None]]) – Raw anomaly scores on the training data.

    • +

  • anomaly_labels – a TimeSeries indicating which timestamps are anomalous. Optional.

    • +

  • post_rule_train_config – The config to use for training the model’s post-rule. The model’s default +post-rule train config is used if none is supplied here.

    • +
+
+
Return type
+

TimeSeries

+
+
+
+ +
+
+get_anomaly_score(time_series, time_series_prev=None, exog_data=None)
+

Returns the model’s predicted sequence of anomaly scores.

+
+
Parameters
+
    +

  • time_series (TimeSeries) – the TimeSeries we wish to predict anomaly scores +for.

    • +

  • time_series_prev (Optional[TimeSeries]) – a TimeSeries immediately preceding +time_series. If given, we use it to initialize the time series +anomaly detection model. Otherwise, we assume that time_series +immediately follows the training data.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

a univariate TimeSeries of anomaly scores

+
+
+
+ +
+
+get_anomaly_label(time_series, time_series_prev=None, exog_data=None)
+

Returns the model’s predicted sequence of anomaly scores, processed +by any relevant post-rules (calibration and/or thresholding).

+
+
Parameters
+
    +

  • time_series (TimeSeries) – the TimeSeries we wish to predict anomaly scores +for.

    • +

  • time_series_prev (Optional[TimeSeries]) – a TimeSeries immediately preceding +time_series. If given, we use it to initialize the time series +anomaly detection model. Otherwise, we assume that time_series +immediately follows the training data.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

a univariate TimeSeries of anomaly scores, filtered by the +model’s post-rule

+
+
+
+ +
+
+get_figure(*, time_series=None, time_stamps=None, time_series_prev=None, exog_data=None, plot_anomaly=True, filter_scores=True, plot_forecast=False, plot_forecast_uncertainty=False, plot_time_series_prev=False)
+
+
Parameters
+
    +

  • time_series (Optional[TimeSeries]) – the time series over whose timestamps we wish to make a forecast. Exactly one of +time_series or time_stamps should be provided.

    • +

  • time_stamps (Optional[List[int]]) – Either a list of timestamps we wish to forecast for, or the number of steps (int) +we wish to forecast for. Exactly one of time_series or time_stamps should be provided.

    • +

  • time_series_prev (Optional[TimeSeries]) – a time series immediately preceding time_series. If given, we use it to initialize +the forecaster’s state. Otherwise, we assume that time_series immediately follows the training data.

    • +

  • exog_data (Optional[TimeSeries]) – A time series of exogenous variables. Exogenous variables are known a priori, and they are +independent of the variable being forecasted. exog_data must include data for all of time_stamps; +if time_series_prev is given, it must include data for all of time_series_prev.time_stamps as well. +Optional. Only supported for models which inherit from ForecasterExogBase.

    • +

  • plot_anomaly – Whether to plot the model’s predicted anomaly scores.

    • +

  • filter_scores – whether to filter the anomaly scores by the post-rule before plotting them.

    • +

  • plot_forecast – Whether to plot the model’s forecasted values.

    • +

  • plot_forecast_uncertainty – whether to plot uncertainty estimates (the inter-quartile range) for forecast +values. Not supported for all models.

    • +

  • plot_time_series_prev – whether to plot time_series_prev (and the model’s fit for it). Only used if +time_series_prev is given.

    • +
+
+
Return type
+

Figure

+
+
Returns
+

a Figure of the model’s anomaly score predictions and/or forecast.

+
+
+
+ +
+
+plot_anomaly(time_series, time_series_prev=None, exog_data=None, *, filter_scores=True, plot_forecast=False, plot_forecast_uncertainty=False, plot_time_series_prev=False, figsize=(1000, 600), ax=None)
+

Plots the time series in matplotlib as a line graph, with points in the +series overlaid as points color-coded to indicate their severity as +anomalies. Optionally allows you to overlay the model’s forecast & the +model’s uncertainty in its forecast (if applicable).

+
+
Parameters
+
    +

  • time_series (TimeSeries) – the time series over whose timestamps we wish to make a forecast. Exactly one of +time_series or time_stamps should be provided.

    • +

  • time_series_prev (Optional[TimeSeries]) – a time series immediately preceding time_series. If given, we use it to initialize +the forecaster’s state. Otherwise, we assume that time_series immediately follows the training data.

    • +

  • exog_data (Optional[TimeSeries]) – A time series of exogenous variables. Exogenous variables are known a priori, and they are +independent of the variable being forecasted. exog_data must include data for all of time_stamps; +if time_series_prev is given, it must include data for all of time_series_prev.time_stamps as well. +Optional. Only supported for models which inherit from ForecasterExogBase.

    • +

  • filter_scores – whether to filter the anomaly scores by the post-rule before plotting them.

    • +

  • plot_forecast – Whether to plot the model’s forecast, in addition to the anomaly scores.

    • +

  • plot_forecast_uncertainty – whether to plot uncertainty estimates (the inter-quartile range) for forecast +values. Not supported for all models.

    • +

  • plot_time_series_prev – whether to plot time_series_prev (and the model’s fit for it). Only used if +time_series_prev is given.

    • +

  • figsize – figure size in pixels

    • +

  • ax – matplotlib axis to add this plot to

    • +
+
+
Returns
+

matplotlib figure & axes

+
+
+
+ +
+
+plot_anomaly_plotly(time_series, time_series_prev=None, exog_data=None, *, filter_scores=True, plot_forecast=False, plot_forecast_uncertainty=False, plot_time_series_prev=False, figsize=(1000, 600))
+

Plots the time series in matplotlib as a line graph, with points in the +series overlaid as points color-coded to indicate their severity as +anomalies. Optionally allows you to overlay the model’s forecast & the +model’s uncertainty in its forecast (if applicable).

+
+
Parameters
+
    +

  • time_series (TimeSeries) – the time series over whose timestamps we wish to make a forecast. Exactly one of +time_series or time_stamps should be provided.

    • +

  • time_series_prev (Optional[TimeSeries]) – a time series immediately preceding time_series. If given, we use it to initialize +the forecaster’s state. Otherwise, we assume that time_series immediately follows the training data.

    • +

  • exog_data (Optional[TimeSeries]) – A time series of exogenous variables. Exogenous variables are known a priori, and they are +independent of the variable being forecasted. exog_data must include data for all of time_stamps; +if time_series_prev is given, it must include data for all of time_series_prev.time_stamps as well. +Optional. Only supported for models which inherit from ForecasterExogBase.

    • +

  • filter_scores – whether to filter the anomaly scores by the post-rule before plotting them.

    • +

  • plot_forecast – Whether to plot the model’s forecast, in addition to the anomaly scores.

    • +

  • plot_forecast_uncertainty – whether to plot uncertainty estimates (the inter-quartile range) for forecast +values. Not supported for all models.

    • +

  • plot_time_series_prev – whether to plot time_series_prev (and the model’s fit for it). Only used if +time_series_prev is given.

    • +

  • figsize – figure size in pixels

    • +
+
+
Returns
+

plotly figure

+
+
+
+ +
+
+plot_forecast(*, time_series=None, time_stamps=None, time_series_prev=None, exog_data=None, plot_forecast_uncertainty=False, plot_time_series_prev=False, figsize=(1000, 600), ax=None)
+

Plots the forecast for the time series in matplotlib, optionally also +plotting the uncertainty of the forecast, as well as the past values +(both true and predicted) of the time series.

+
+
Parameters
+
    +

  • time_series (Optional[TimeSeries]) – the time series over whose timestamps we wish to make a forecast. Exactly one of +time_series or time_stamps should be provided.

    • +

  • time_stamps (Optional[List[int]]) – Either a list of timestamps we wish to forecast for, or the number of steps (int) +we wish to forecast for. Exactly one of time_series or time_stamps should be provided.

    • +

  • time_series_prev (Optional[TimeSeries]) – a time series immediately preceding time_series. If given, we use it to initialize +the forecaster’s state. Otherwise, we assume that time_series immediately follows the training data.

    • +

  • exog_data (Optional[TimeSeries]) – A time series of exogenous variables. Exogenous variables are known a priori, and they are +independent of the variable being forecasted. exog_data must include data for all of time_stamps; +if time_series_prev is given, it must include data for all of time_series_prev.time_stamps as well. +Optional. Only supported for models which inherit from ForecasterExogBase.

    • +

  • plot_forecast_uncertainty – whether to plot uncertainty estimates (the inter-quartile range) for forecast +values. Not supported for all models.

    • +

  • plot_time_series_prev – whether to plot time_series_prev (and the model’s fit for it). Only used if +time_series_prev is given.

    • +

  • figsize – figure size in pixels

    • +

  • ax – matplotlib axis to add this plot to

    • +
+
+
Returns
+

(fig, ax): matplotlib figure & axes the figure was plotted on

+
+
+
+ +
+
+plot_forecast_plotly(*, time_series=None, time_stamps=None, time_series_prev=None, exog_data=None, plot_forecast_uncertainty=False, plot_time_series_prev=False, figsize=(1000, 600))
+

Plots the forecast for the time series in plotly, optionally also +plotting the uncertainty of the forecast, as well as the past values +(both true and predicted) of the time series.

+
+
Parameters
+
    +

  • time_series (Optional[TimeSeries]) – the time series over whose timestamps we wish to make a forecast. Exactly one of +time_series or time_stamps should be provided.

    • +

  • time_stamps (Optional[List[int]]) – Either a list of timestamps we wish to forecast for, or the number of steps (int) +we wish to forecast for. Exactly one of time_series or time_stamps should be provided.

    • +

  • time_series_prev (Optional[TimeSeries]) – a time series immediately preceding time_series. If given, we use it to initialize +the forecaster’s state. Otherwise, we assume that time_series immediately follows the training data.

    • +

  • exog_data (Optional[TimeSeries]) – A time series of exogenous variables. Exogenous variables are known a priori, and they are +independent of the variable being forecasted. exog_data must include data for all of time_stamps; +if time_series_prev is given, it must include data for all of time_series_prev.time_stamps as well. +Optional. Only supported for models which inherit from ForecasterExogBase.

    • +

  • plot_forecast_uncertainty – whether to plot uncertainty estimates (the +inter-quartile range) for forecast values. Not supported for all +models.

    • +

  • plot_time_series_prev – whether to plot time_series_prev (and +the model’s fit for it). Only used if time_series_prev is given.

    • +

  • figsize – figure size in pixels

    • +
+
+
+
+ +
+ +
+
+

anomaly.forecast_based.arima

+

Classic ARIMA (AutoRegressive Integrated Moving Average) forecasting model, +adapted for anomaly detection.

+
+
+class merlion.models.anomaly.forecast_based.arima.ArimaDetectorConfig(order=(4, 1, 2), seasonal_order=(0, 0, 0, 0), exog_transform: TransformBase = None, exog_aggregation_policy: Union[AggregationPolicy, str] = 'Mean', exog_missing_value_policy: Union[MissingValuePolicy, str] = 'ZFill', max_forecast_steps: int = None, target_seq_index: int = None, invert_transform=None, transform: TransformBase = None, max_score: float = 1000, threshold=None, enable_calibrator=True, enable_threshold=True, **kwargs)
+

Bases: ArimaConfig, DetectorConfig

+

Configuration class for Arima. Just a Sarima model with seasonal order (0, 0, 0, 0).

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • order – Order is (p, d, q) for an ARIMA(p, d, q) process. d must +be an integer indicating the integration order of the process, while +p and q must be integers indicating the AR and MA orders (so that +all lags up to those orders are included).

    • +

  • seasonal_order – (0, 0, 0, 0) because ARIMA has no seasonal order.

    • +

  • exog_transform – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • max_forecast_steps – Max # of steps we would like to forecast for. Required for some models like MSES.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.forecast_based.arima.ArimaDetector(config)
+

Bases: ForecastingDetectorBase, Arima

+
+
+config_class
+

alias of ArimaDetectorConfig

+
+ +
+ +
+
+

anomaly.forecast_based.sarima

+

Seasonal ARIMA (SARIMA) forecasting model, adapted for anomaly detection.

+
+
+class merlion.models.anomaly.forecast_based.sarima.SarimaDetectorConfig(order=(4, 1, 2), seasonal_order=(2, 0, 1, 24), exog_transform=None, exog_aggregation_policy='Mean', exog_missing_value_policy='ZFill', max_forecast_steps=None, target_seq_index=None, invert_transform=None, transform=None, max_score=1000, threshold=None, enable_calibrator=True, enable_threshold=True, **kwargs)
+

Bases: SarimaConfig, DetectorConfig

+

Config class for Sarima (Seasonal AutoRegressive Integrated Moving Average).

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • order (List[int]) – Order is (p, d, q) for an ARIMA(p, d, q) process. d must +be an integer indicating the integration order of the process, while +p and q must be integers indicating the AR and MA orders (so that +all lags up to those orders are included).

    • +

  • seasonal_order (List[int]) – Seasonal order is (P, D, Q, S) for seasonal ARIMA +process, where s is the length of the seasonality cycle (e.g. s=24 +for 24 hours on hourly granularity). P, D, Q are as for ARIMA.

    • +

  • exog_transform – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • max_forecast_steps – Max # of steps we would like to forecast for. Required for some models like MSES.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.forecast_based.sarima.SarimaDetector(config)
+

Bases: ForecastingDetectorBase, Sarima

+
+
+config_class
+

alias of SarimaDetectorConfig

+
+ +
+ +
+
+

anomaly.forecast_based.ets

+

ETS (error, trend, seasonal) forecasting model, adapted for anomaly detection.

+
+
+class merlion.models.anomaly.forecast_based.ets.ETSDetectorConfig(max_forecast_steps=None, target_seq_index=None, error='add', trend='add', damped_trend=True, seasonal='add', seasonal_periods=None, refit=True, invert_transform=None, transform=None, enable_calibrator=False, max_score=1000, threshold=None, enable_threshold=True, **kwargs)
+

Bases: ETSConfig, NoCalibrationDetectorConfig

+

Configuration class for ETS model. ETS model is an underlying state space +model consisting of an error term (E), a trend component (T), a seasonal +component (S), and a level component. Each component is flexible with +different traits with additive (‘add’) or multiplicative (‘mul’) formulation. +Refer to https://otexts.com/fpp2/taxonomy.html for more information +about ETS model.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • max_forecast_steps (Optional[int]) – Number of steps we would like to forecast for.

    • +

  • target_seq_index (Optional[int]) – The index of the univariate (amongst all +univariates in a general multivariate time series) whose value we +would like to forecast.

    • +

  • error (str) – The error term. “add” or “mul”.

    • +

  • trend (str) – The trend component. “add”, “mul” or None.

    • +

  • damped_trend (bool) – Whether or not an included trend component is damped.

    • +

  • seasonal (str) – The seasonal component. “add”, “mul” or None.

    • +

  • seasonal_periods (Optional[int]) – The length of the seasonality cycle. None by default.

    • +

  • refit (bool) – if True, refit the full ETS model when time_series_prev is given to the forecast method +(slower). If False, simply perform exponential smoothing (faster).

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • enable_calibrator – False because this config assumes calibrated outputs from the model.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.forecast_based.ets.ETSDetector(config)
+

Bases: ForecastingDetectorBase, ETS

+
+
+config_class
+

alias of ETSDetectorConfig

+
+ +
+ +
+
+

anomaly.forecast_based.prophet

+

Adaptation of Facebook’s Prophet forecasting model to anomaly detection.

+
+
+class merlion.models.anomaly.forecast_based.prophet.ProphetDetectorConfig(max_forecast_steps=None, target_seq_index=None, yearly_seasonality='auto', weekly_seasonality='auto', daily_seasonality='auto', seasonality_mode='additive', holidays=None, uncertainty_samples=100, exog_transform=None, exog_aggregation_policy='Mean', exog_missing_value_policy='ZFill', invert_transform=None, transform=None, max_score=1000, threshold=None, enable_calibrator=True, enable_threshold=True, **kwargs)
+

Bases: ProphetConfig, DetectorConfig

+

Configuration class for Facebook’s Prophet model, as described by +Taylor & Letham, 2017.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • max_forecast_steps (Optional[int]) – Max # of steps we would like to forecast for.

    • +

  • target_seq_index (Optional[int]) – The index of the univariate (amongst all +univariates in a general multivariate time series) whose value we +would like to forecast.

    • +

  • yearly_seasonality (Union[bool, int]) – If bool, whether to enable yearly seasonality. +By default, it is activated if there are >= 2 years of history, but +deactivated otherwise. If int, this is the number of Fourier series +components used to model the seasonality (default = 10).

    • +

  • weekly_seasonality (Union[bool, int]) – If bool, whether to enable weekly seasonality. +By default, it is activated if there are >= 2 weeks of history, but +deactivated otherwise. If int, this is the number of Fourier series +components used to model the seasonality (default = 3).

    • +

  • daily_seasonality (Union[bool, int]) – If bool, whether to enable daily seasonality. +By default, it is activated if there are >= 2 days of history, but +deactivated otherwise. If int, this is the number of Fourier series +components used to model the seasonality (default = 4).

    • +

  • seasonality_mode – ‘additive’ (default) or ‘multiplicative’.

    • +

  • holidays – pd.DataFrame with columns holiday (string) and ds (date type) +and optionally columns lower_window and upper_window which specify a +range of days around the date to be included as holidays. +lower_window=-2 will include 2 days prior to the date as holidays. Also +optionally can have a column prior_scale specifying the prior scale for +that holiday. Can also be a dict corresponding to the desired pd.DataFrame.

    • +

  • uncertainty_samples (int) – The number of posterior samples to draw in +order to calibrate the anomaly scores.

    • +

  • exog_transform – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.forecast_based.prophet.ProphetDetector(config)
+

Bases: ForecastingDetectorBase, Prophet

+
+
+config_class
+

alias of ProphetDetectorConfig

+
+ +
+ +
+
+

anomaly.forecast_based.mses

+

MSES (Multi-Scale Exponential Smoother) forecasting model adapted for anomaly detection.

+
+
+class merlion.models.anomaly.forecast_based.mses.MSESDetectorConfig(max_forecast_steps, online_updates=True, max_backstep=None, recency_weight=0.5, accel_weight=1.0, optimize_acc=True, eta=0.0, rho=0.0, phi=2.0, inflation=1.0, target_seq_index=None, invert_transform=None, transform=None, max_score=1000, threshold=None, enable_calibrator=True, enable_threshold=True, **kwargs)
+

Bases: MSESConfig, DetectorConfig

+

Configuration class for an MSES forecasting model adapted for anomaly detection.

+

Letting w be the recency weight, B the maximum backstep, x_t the last seen data point, +and l_s,t the series of losses for scale s.

+
+\[\begin{split}\begin{align*} +\hat{x}_{t+h} & = \sum_{b=0}^B p_{b} \cdot (x_{t-b} + v_{b+h,t} + a_{b+h,t}) \\ +\space \\ +\text{where} \space\space & v_{b+h,t} = \text{EMA}_w(\Delta_{b+h} x_t) \\ +& a_{b+h,t} = \text{EMA}_w(\Delta_{b+h}^2 x_t) \\ +\text{and} \space\space & p_b = \sigma(z)_b \space\space \\ +\text{if} & \space\space z_b = (b+h)^\phi \cdot \text{EMA}_w(l_{b+h,t}) \cdot \text{RWSE}_w(l_{b+h,t})\\ +\end{align*}\end{split}\]
+
+
Parameters
+
    +

  • max_forecast_steps (int) – Max # of steps we would like to forecast for. Required for some models like MSES.

    • +

  • max_backstep – Max backstep to use in forecasting. If we train with x(0),…,x(t), +Then, the b-th model MSES uses will forecast x(t+h) by anchoring at x(t-b) and +predicting xhat(t+h) = x(t-b) + delta_hat(b+h).

    • +

  • recency_weight – The recency weight parameter to use when estimating delta_hat.

    • +

  • accel_weight – The weight to scale the acceleration by when computing delta_hat. +Specifically, delta_hat(b+h) = velocity(b+h) + accel_weight * acceleration(b+h).

    • +

  • optimize_acc – If True, the acceleration correction will only be used at scales +ranging from 1,…(max_backstep+max_forecast_steps)/2.

    • +

  • eta – The parameter used to control the rate at which recency_weight gets +tuned when online updates are made to the model and losses can be computed.

    • +

  • rho – The parameter that determines what fraction of the overall error is due to +velcity error, while the rest is due to the complement. The error at any scale +will be determined as rho * velocity_error + (1-rho) * loss_error.

    • +

  • phi – The parameter used to exponentially inflate the magnitude of loss error at +different scales. Loss error for scale s will be increased by a factor of phi ** s.

    • +

  • inflation – The inflation exponent to use when computing the distribution +p(b|h) over the models when forecasting at horizon h according to standard +errors of the estimated velocities over the models; inflation=1 is equivalent +to using the softmax function.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.forecast_based.mses.MSESDetector(config)
+

Bases: ForecastingDetectorBase, MSES

+
+
Parameters
+

config (MSESConfig) – model configuration

+
+
+
+
+config_class
+

alias of MSESDetectorConfig

+
+ +
+
+property online_updates
+
+ +
+
+get_anomaly_score(time_series, time_series_prev=None, exog_data=None)
+

Returns the model’s predicted sequence of anomaly scores.

+
+
Parameters
+
    +

  • time_series (TimeSeries) – the TimeSeries we wish to predict anomaly scores +for.

    • +

  • time_series_prev (Optional[TimeSeries]) – a TimeSeries immediately preceding +time_series. If given, we use it to initialize the time series +anomaly detection model. Otherwise, we assume that time_series +immediately follows the training data.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

a univariate TimeSeries of anomaly scores

+
+
+
+ +
+ +
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.models.anomaly.html b/v2.0.2/merlion.models.anomaly.html new file mode 100644 index 000000000..88f3c837b --- /dev/null +++ b/v2.0.2/merlion.models.anomaly.html @@ -0,0 +1,2310 @@ + + + + + + anomaly — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ + + +
+

anomaly

+

Contains all anomaly detection models. Forecaster-based anomaly detection models +may be found in merlion.models.anomaly.forecast_based. Change-point detection models may be +found in merlion.models.anomaly.change_point.

+

For anomaly detection, we define an abstract DetectorBase class which inherits from ModelBase and supports the +following interface, in addition to model.save and DetectorClass.load defined for ModelBase:

+
    +

  1. model = DetectorClass(config)

    +
      +

    • initialization with a model-specific config

      • +

    • configs contain:

      +
      +
        +

      • a (potentially trainable) data pre-processing transform from merlion.transform; +note that model.transform is a property which refers to model.config.transform

        • +

      • a (potentially trainable) post-processing rule from merlion.post_process; +note that model.post_rule is a property which refers to model.config.post_rule. +In general, this post-rule will have two stages: calibration +and thresholding.

        • +

      • booleans enable_calibrator and enable_threshold (both defaulting to True) indicating +whether to enable calibration and thresholding in the post-rule.

        • +

      • model-specific hyperparameters

        • +
      +
      +
      • +
    +
    2. +

  2. model.get_anomaly_score(time_series, time_series_prev=None)

    +
      +

    • returns a time series of anomaly scores for each timestamp in time_series

      • +

    • time_series_prev (optional): the most recent context, only used for some models. If not provided, the +training data is used as the context instead.

      • +
    +
    3. +

  3. model.get_anomaly_label(time_series, time_series_prev=None)

    +
      +

    • returns a time series of post-processed anomaly scores for each timestamp in time_series. These scores +are calibrated to correspond to z-scores if enable_calibrator is True, and they have also been filtered +by a thresholding rule (model.threshold) if enable_threshold is True. threshold is specified +manually in the config (though it may be modified by DetectorBase.train), .

      • +

    • time_series_prev (optional): the most recent context, only used for some models. If not provided, the +training data is used as the context instead.

      • +
    +
    4. +

  4. model.train(train_data, anomaly_labels=None, train_config=None, post_rule_train_config=None)

    +
      +

    • trains the model on the time series train_data

      • +

    • anomaly_labels (optional): a time series aligned with train_data, which indicates whether each +time stamp is anomalous

      • +

    • train_config (optional): extra configuration describing how the model should be trained. +Not used for all models. Class-level default provided for models which do use it.

      • +

    • post_rule_train_config: extra configuration describing how to train the model’s post-rule. Class-level +default is provided for all models.

      • +

    • returns a time series of anomaly scores produced by the model on train_data.

      • +
    +
    5. +
+

Base classes

+ ++++ + + + + + +

base

Base class for anomaly detectors.

+

Univariate models:

+ ++++ + + + + + + + + + + + + + + + + + +

dbl

Dynamic Baseline anomaly detection model for time series with daily, weekly or monthly trends.

windstats

Window Statistics anomaly detection model for data with weekly seasonality.

spectral_residual

Spectral Residual algorithm for anomaly detection

stat_threshold

Simple static thresholding model for anomaly detection.

zms

Multiple z-score model (static thresholding at multiple time scales).

+

Multivariate models:

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + +

isolation_forest

The classic isolation forest model for anomaly detection.

random_cut_forest

Wrapper around AWS's Random Cut Forest anomaly detection model.

autoencoder

The autoencoder-based anomaly detector for multivariate time series

dagmm

Deep autoencoding Gaussian mixture model for anomaly detection (DAGMM)

lstm_ed

The LSTM-encoder-decoder-based anomaly detector for multivariate time series

vae

The VAE-based anomaly detector for multivariate time series

deep_point_anomaly_detector

Deep Point Anomaly Detector algorithm.

+
+

Subpackages

+
+ +
+
+
+

Base classes

+
+

anomaly.base

+

Base class for anomaly detectors.

+
+
+class merlion.models.anomaly.base.DetectorConfig(max_score=1000, threshold=None, enable_calibrator=True, enable_threshold=True, transform=None, **kwargs)
+

Bases: Config

+

Config object used to define an anomaly detection model.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • max_score (float) – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+
+enable_threshold: bool = True
+
+ +
+
+enable_calibrator: bool = True
+
+ +
+
+calibrator: AnomScoreCalibrator = None
+
+ +
+
+threshold: Threshold = None
+
+ +
+
+property post_rule
+
+
Returns
+

The full post-processing rule. Includes calibration if +enable_calibrator is True, followed by thresholding if +enable_threshold is True.

+
+
+
+ +
+
+classmethod from_dict(config_dict, return_unused_kwargs=False, calibrator=None, **kwargs)
+

Constructs a Config from a Python dictionary of parameters.

+
+
Parameters
+
    +

  • config_dict (Dict[str, Any]) – dict that will be used to instantiate this object.

    • +

  • return_unused_kwargs – whether to return any unused keyword args.

    • +

  • dim – the dimension of the time series. handled as a special case.

    • +

  • kwargs – any additional parameters to set (overriding config_dict).

    • +
+
+
Returns
+

Config object initialized from the dict.

+
+
+
+ +
+ +
+
+class merlion.models.anomaly.base.NoCalibrationDetectorConfig(enable_calibrator=False, max_score: float = 1000, threshold=None, enable_threshold=True, transform: TransformBase = None, **kwargs)
+

Bases: DetectorConfig

+

Abstract config object for an anomaly detection model that should never +perform anomaly score calibration.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • enable_calibrator – False because this config assumes calibrated outputs from the model.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+
+property calibrator
+
+
Returns
+

None

+
+
+
+ +
+
+property enable_calibrator
+
+
Returns
+

False

+
+
+
+ +
+ +
+
+class merlion.models.anomaly.base.DetectorBase(config)
+

Bases: ModelBase

+

Base class for an anomaly detection model.

+
+
Parameters
+

config (DetectorConfig) – model configuration

+
+
+
+
+config_class
+

alias of DetectorConfig

+
+ +
+
+property threshold
+
+ +
+
+property calibrator
+
+ +
+
+property post_rule
+
+ +
+
+train(train_data, train_config=None, anomaly_labels=None, post_rule_train_config=None)
+

Trains the anomaly detector (unsupervised) and its post-rule (supervised, if labels are given) on train data.

+
+
Parameters
+
    +

  • train_data (TimeSeries) – a TimeSeries of metric values to train the model.

    • +

  • train_config – Additional training configs, if needed. Only required for some models.

    • +

  • anomaly_labels (Optional[TimeSeries]) – a TimeSeries indicating which timestamps are anomalous. Optional.

    • +

  • post_rule_train_config – The config to use for training the model’s post-rule. The model’s default +post-rule train config is used if none is supplied here.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

A TimeSeries of the model’s anomaly scores on the training data.

+
+
+
+ +
+
+train_post_process(train_result, anomaly_labels=None, post_rule_train_config=None)
+

Converts the train result (anom scores on train data) into a TimeSeries object and trains the post-rule.

+
+
Parameters
+
    +

  • train_result (Union[TimeSeries, DataFrame]) – Raw anomaly scores on the training data.

    • +

  • anomaly_labels – a TimeSeries indicating which timestamps are anomalous. Optional.

    • +

  • post_rule_train_config – The config to use for training the model’s post-rule. The model’s default +post-rule train config is used if none is supplied here.

    • +
+
+
Return type
+

TimeSeries

+
+
+
+ +
+
+get_anomaly_score(time_series, time_series_prev=None)
+

Returns the model’s predicted sequence of anomaly scores.

+
+
Parameters
+
    +

  • time_series (TimeSeries) – the TimeSeries we wish to predict anomaly scores +for.

    • +

  • time_series_prev (Optional[TimeSeries]) – a TimeSeries immediately preceding +time_series. If given, we use it to initialize the time series +anomaly detection model. Otherwise, we assume that time_series +immediately follows the training data.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

a univariate TimeSeries of anomaly scores

+
+
+
+ +
+
+get_anomaly_label(time_series, time_series_prev=None)
+

Returns the model’s predicted sequence of anomaly scores, processed +by any relevant post-rules (calibration and/or thresholding).

+
+
Parameters
+
    +

  • time_series (TimeSeries) – the TimeSeries we wish to predict anomaly scores +for.

    • +

  • time_series_prev (Optional[TimeSeries]) – a TimeSeries immediately preceding +time_series. If given, we use it to initialize the time series +anomaly detection model. Otherwise, we assume that time_series +immediately follows the training data.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

a univariate TimeSeries of anomaly scores, filtered by the +model’s post-rule

+
+
+
+ +
+
+get_figure(time_series, time_series_prev=None, *, filter_scores=True, plot_time_series_prev=False, fig=None, **kwargs)
+
+
Parameters
+
    +

  • time_series (TimeSeries) – The TimeSeries we wish to plot & predict anomaly scores for.

    • +

  • time_series_prev (Optional[TimeSeries]) – a TimeSeries immediately preceding +time_stamps. If given, we use it to initialize the time series +model. Otherwise, we assume that time_stamps immediately follows +the training data.

    • +

  • filter_scores – whether to filter the anomaly scores by the +post-rule before plotting them.

    • +

  • plot_time_series_prev – whether to plot time_series_prev (and +the model’s fit for it). Only used if time_series_prev is given.

    • +

  • fig (Optional[Figure]) – a Figure we might want to add anomaly scores onto.

    • +
+
+
Return type
+

Figure

+
+
Returns
+

a Figure of the model’s anomaly score predictions.

+
+
+
+ +
+
+plot_anomaly(time_series, time_series_prev=None, *, filter_scores=True, plot_time_series_prev=False, figsize=(1000, 600), ax=None)
+

Plots the time series in matplotlib as a line graph, with points in the +series overlaid as points color-coded to indicate their severity as +anomalies.

+
+
Parameters
+
    +

  • time_series (TimeSeries) – The TimeSeries we wish to plot & predict anomaly scores for.

    • +

  • time_series_prev (Optional[TimeSeries]) – a TimeSeries immediately preceding +time_series. Plotted as context if given.

    • +

  • filter_scores – whether to filter the anomaly scores by the +post-rule before plotting them.

    • +

  • plot_time_series_prev – whether to plot time_series_prev (and +the model’s fit for it). Only used if time_series_prev is given.

    • +

  • figsize – figure size in pixels

    • +

  • ax – matplotlib axes to add this plot to

    • +
+
+
Returns
+

matplotlib figure & axes

+
+
+
+ +
+
+plot_anomaly_plotly(time_series, time_series_prev=None, *, filter_scores=True, plot_time_series_prev=False, figsize=None)
+

Plots the time series in plotly as a line graph, with points in the +series overlaid as points color-coded to indicate their severity as +anomalies.

+
+
Parameters
+
    +

  • time_series (TimeSeries) – The TimeSeries we wish to plot & predict anomaly scores for.

    • +

  • time_series_prev (Optional[TimeSeries]) – a TimeSeries immediately preceding +time_series. Plotted as context if given.

    • +

  • filter_scores – whether to filter the anomaly scores by the +post-rule before plotting them.

    • +

  • plot_time_series_prev – whether to plot time_series_prev (and +the model’s fit for it). Only used if time_series_prev is given.

    • +

  • figsize – figure size in pixels

    • +
+
+
Returns
+

plotly figure

+
+
+
+ +
+ +
+
+class merlion.models.anomaly.base.MultipleTimeseriesDetectorMixin
+

Bases: MultipleTimeseriesModelMixin

+

Abstract mixin for anomaly detectors supporting training on multiple time series.

+
+
+abstract train_multiple(multiple_train_data, train_config=None, anomaly_labels=None, post_rule_train_config=None)
+

Trains the anomaly detector (unsupervised) and its post-rule +(supervised, if labels are given) on the input multiple time series.

+
+
Parameters
+
    +

  • multiple_train_data (List[TimeSeries]) – a list of TimeSeries of metric values to train the model.

    • +

  • anomaly_labels (Optional[List[TimeSeries]]) – a list of TimeSeries indicating which timestamps are anomalous. Optional.

    • +

  • train_config – Additional training configs, if needed. Only required for some models.

    • +

  • post_rule_train_config – The config to use for training the +model’s post-rule. The model’s default post-rule train config is +used if none is supplied here.

    • +
+
+
Return type
+

List[TimeSeries]

+
+
Returns
+

A list of TimeSeries of the model’s anomaly scores on the training +data with each element corresponds to time series from multiple_train_data.

+
+
+
+ +
+ +
+
+
+

Univariate models

+
+

anomaly.dbl

+

Dynamic Baseline anomaly detection model for time series with daily, weekly or monthly trends.

+
+
+class merlion.models.anomaly.dbl.DynamicBaselineConfig(fixed_period=None, train_window=None, wind_sz='1h', trends=None, max_score=1000, threshold=None, enable_calibrator=True, enable_threshold=True, transform=None, **kwargs)
+

Bases: DetectorConfig

+

Configuration class for DynamicBaseline.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • fixed_period (Optional[Tuple[str, str]]) – (t0, tf); Train the model on all datapoints +occurring between t0 and tf (inclusive).

    • +

  • train_window (Optional[str]) – A string representing a duration of time to serve as +the scope for a rolling dynamic baseline model.

    • +

  • wind_sz (str) – The window size in minutes to bucket times of day. This +parameter only applied if a daily trend is one of the trends used.

    • +

  • trends (Optional[List[str]]) – The list of trends to use. Supported trends are “daily”, +“weekly” and “monthly”.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+
+property fixed_period
+
+ +
+
+property trends
+
+ +
+
+determine_train_window()
+
+ +
+
+to_dict(_skipped_keys=None)
+
+
Returns
+

dict with keyword arguments used to initialize the config class.

+
+
+
+ +
+ +
+
+class merlion.models.anomaly.dbl.DynamicBaseline(config)
+

Bases: DetectorBase

+

Dynamic baseline-based anomaly detector.

+

Detects anomalies by comparing data to historical data that has occurred in +the same window of time, as defined by any combination of time of day, +day of week, or day of month.

+

A DBL model can have a fixed period or a dynamic rolling period. A fixed +period model trains its baselines exclusively on datapoints occurring in the +fixed period, while a rolling period model trains continually on the most +recent datapoints within its train-window.

+
+
Parameters
+

config (DynamicBaselineConfig) – model configuration

+
+
+
+
+config_class
+

alias of DynamicBaselineConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+
+property train_window
+
+ +
+
+property fixed_period
+
+ +
+
+property has_fixed_period
+
+ +
+
+property data: UnivariateTimeSeries
+
+ +
+
+get_relevant(data)
+

Returns the subset of the data that should be used for training +or updating.

+
+ +
+
+get_baseline(time_stamps)
+

Returns the dynamic baselines corresponding to the time stamps +:type time_stamps: List[float] +:param time_stamps: a list of timestamps

+
+
Return type
+

Tuple[UnivariateTimeSeries, UnivariateTimeSeries]

+
+
+
+ +
+
+update(new_data)
+
+ +
+
+get_baseline_figure(time_series, time_series_prev=None, *, filter_scores=True, plot_time_series_prev=False, fig=None, jitter_time_stamps=True)
+
+
Return type
+

Figure

+
+
+
+ +
+ +
+
+class merlion.models.anomaly.dbl.Trend(value)
+

Bases: Enum

+

Enumeration of the supported trends.

+
+
+daily = 1
+
+ +
+
+weekly = 2
+
+ +
+
+monthly = 3
+
+ +
+ +
+
+class merlion.models.anomaly.dbl.Segment(key)
+

Bases: object

+

Class representing a segment. The class maintains a mean (baseline) +along with a variance so that a z-score can be computed.

+
+
+add(x)
+
+ +
+
+drop(x)
+
+ +
+
+score(x)
+
+ +
+ +
+
+class merlion.models.anomaly.dbl.Segmenter(trends, wind_sz)
+

Bases: object

+

Class for managing the segments that belong to a DynamicBaseline model.

+
+
Parameters
+
    +

  • trends (List[Trend]) – A list of trend types to create segments based on.

    • +

  • wind_sz (str) – The window size in minutes to bucket times of day. +Only used if a daily trend is one of the trends used.

    • +
+
+
+
+
+day_delta = Timedelta('1 days 00:00:00')
+
+ +
+
+hour_delta = Timedelta('0 days 01:00:00')
+
+ +
+
+min_delta = Timedelta('0 days 00:01:00')
+
+ +
+
+zero_delta = Timedelta('0 days 00:00:00')
+
+ +
+
+reset()
+
+ +
+
+property wind_delta
+
+ +
+
+property trends
+
+ +
+
+property trend
+
+ +
+
+window_key(t)
+
+ +
+
+weekday_key(t)
+
+ +
+
+day_key(t)
+
+ +
+
+segment_key(timestamp)
+
+ +
+
+add(t, x)
+
+ +
+
+drop(t, x)
+
+ +
+
+score(t, x)
+
+ +
+
+get_baseline(t)
+
+
Return type
+

Tuple[float, float]

+
+
+
+ +
+ +
+
+

anomaly.windstats

+

Window Statistics anomaly detection model for data with weekly seasonality.

+
+
+class merlion.models.anomaly.windstats.WindStatsConfig(wind_sz=30, max_day=4, max_score: float = 1000, threshold=None, enable_calibrator=True, enable_threshold=True, transform: TransformBase = None, **kwargs)
+

Bases: DetectorConfig

+

Config class for WindStats.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • wind_sz – the window size in minutes, default is 30 minute window

    • +

  • max_day – maximum number of week days stored in memory (only mean +and std of each window are stored). Here, the days are first +bucketed by weekday and then by window id.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.windstats.WindStats(config=None)
+

Bases: DetectorBase

+

Sliding Window Statistics based Anomaly Detector. +This detector assumes the time series comes with a weekly seasonality. +It divides the week into buckets of the specified size (in minutes). For +a given (t, v) it computes an anomaly score by comparing the current +value v against the historical values (mean and standard deviation) for +that window of time. +Note that if multiple matches (specified by the parameter max_day) can be +found in history with the same weekday and same time window, then the +minimum of the scores is returned.

+

config.wind_sz: the window size in minutes, default is 30 minute window +config.max_days: maximum number of week days stored in memory (only mean and std of each window are stored) +here the days are first bucketized by weekday and then bucketized by window id.

+
+
+config_class
+

alias of WindStatsConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+ +
+
+

anomaly.spectral_residual

+

Spectral Residual algorithm for anomaly detection

+
+
+class merlion.models.anomaly.spectral_residual.SpectralResidualConfig(local_wind_sz=21, q=3, estimated_points=5, predicting_points=5, target_seq_index=None, max_score: float = 1000, threshold=None, enable_calibrator=True, enable_threshold=True, transform: TransformBase = None, **kwargs)
+

Bases: DetectorConfig

+

Config class for SpectralResidual anomaly detector.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • local_wind_sz – Number of previous saliency points to consider when computing the anomaly score

    • +

  • q – Window size of local frequency average computations

    • +

  • estimated_points – Number of padding points to add to the timeseries for saliency map calculations.

    • +

  • predicting_points – Number of points to consider when computing gradient for padding points

    • +

  • target_seq_index – Index of the univariate whose anomalies we want to detect.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+

The Saliency Map is computed as follows:

+
+\[\begin{split}R(f) &= \log(A(\mathscr{F}(\textbf{x}))) - \left(\frac{1}{q}\right)_{1 \times q} +* (A(\mathscr{F}(\textbf{x})) \\ +S_m &= \mathscr{F}^{-1} (R(f))\end{split}\]
+

where \(*\) is the convolution operator, and \(\mathscr{F}\) is the Fourier Transform. +The anomaly scores then are computed as:

+
+\[S(x) = \frac{S(x) - \overline{S(\textbf{x})}}{\overline{S(\textbf{x})}}\]
+

where \(\textbf{x}\) are the last local_wind_sz points in the timeseries.

+

The estimated_points and predicting_points parameters are used to pad the end of the timeseries with reasonable +values. This is done so that the later points in the timeseries are in the middle of averaging windows rather +than in the end.

+
+ +
+
+class merlion.models.anomaly.spectral_residual.SpectralResidual(config=None)
+

Bases: DetectorBase

+

Spectral Residual Algorithm for Anomaly Detection.

+

Spectral Residual Anomaly Detection algorithm based on the algorithm described by +Ren et al. (2019). After taking the frequency spectrum, compute the +log deviation from the mean. Use inverse fourier transform to obtain the saliency map. Anomaly scores +for a point in the time series are obtained by comparing the saliency score of the point to the +average of the previous points.

+
+
Parameters
+

config (Optional[SpectralResidualConfig]) – model configuration

+
+
+
+
+config_class
+

alias of SpectralResidualConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+
+property target_seq_index: int
+
+ +
+ +
+
+

anomaly.stat_threshold

+

Simple static thresholding model for anomaly detection.

+
+
+class merlion.models.anomaly.stat_threshold.StatThresholdConfig(target_seq_index=None, max_score=1000, threshold=None, enable_calibrator=True, enable_threshold=True, transform=None, normalize=None, **kwargs)
+

Bases: DetectorConfig, NormalizingConfig

+

Config class for StatThreshold.

+
+
Parameters
+
    +

  • (optional) (target_seq_index) – The index of the univariate whose value we are considering thresholds of. +If not provided, the model only works for univariate data.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • normalize – Pre-trained normalization transformation (optional).

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.stat_threshold.StatThreshold(config)
+

Bases: DetectorBase

+

Anomaly detection based on a static threshold.

+
+
Parameters
+

config (DetectorConfig) – model configuration

+
+
+
+
+config_class
+

alias of StatThresholdConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+ +
+
+

anomaly.zms

+

Multiple z-score model (static thresholding at multiple time scales).

+
+
+class merlion.models.anomaly.zms.ZMSConfig(base=2, n_lags=None, lag_inflation=1.0, max_score=1000, threshold=None, enable_calibrator=True, enable_threshold=True, transform=None, normalize=None, **kwargs)
+

Bases: DetectorConfig, NormalizingConfig

+

Configuration class for ZMS anomaly detection model. The transform of this config is actually a +pre-processing step, followed by the desired number of lag transforms, and a final mean/variance +normalization step. This full transform may be accessed as ZMSConfig.full_transform. Note that +the normalization is inherited from NormalizingConfig.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • base (int) – The base to use for computing exponentially distant lags.

    • +

  • n_lags (Optional[int]) – The number of lags to be used. If None, n_lags will be +chosen later as the maximum number of lags possible for the initial +training set.

    • +

  • lag_inflation (float) – See math below for the precise mathematical role of +the lag inflation. Consider the lag inflation a measure of distrust +toward higher lags, If lag_inflation > 1, the higher the lag +inflation, the less likely the model is to select a higher lag’s z-score +as the anomaly score.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • normalize – Pre-trained normalization transformation (optional).

    • +
+
+
+
+\[\begin{split}\begin{align*} +\text{Let } \space z_k(x_t) \text{ be the z-score of the } & k\text{-lag at } t, \space \Delta_k(x_t) +\text{ and } p \text{ be the lag inflation} \\ +& \\ +\text{the anomaly score } z(x_t) & = z_{k^*}(x_t) \\ +\text{where } k^* & = \text{argmax}_k \space | z_k(x_t) | / k^p +\end{align*}\end{split}\]
+
+
+property full_transform
+

Returns the full transform, including the pre-processing step, lags, and +final mean/variance normalization.

+
+ +
+
+to_dict(_skipped_keys=None)
+
+
Returns
+

dict with keyword arguments used to initialize the config class.

+
+
+
+ +
+
+property n_lags
+
+ +
+ +
+
+class merlion.models.anomaly.zms.ZMS(config)
+

Bases: DetectorBase

+

Multiple Z-Score based Anomaly Detector.

+

ZMS is designed to detect spikes, dips, sharp trend changes (up or down) +relative to historical data. Anomaly scores capture not only magnitude +but also direction. This lets one distinguish between positive (spike) +negative (dip) anomalies for example.

+

The algorithm builds models of normalcy at multiple exponentially-growing +time scales. The zeroth order model is just a model of the values seen +recently. The kth order model is similar except that it models not +values, but rather their k-lags, defined as x(t)-x(t-k), for k in +1, 2, 4, 8, 16, etc. The algorithm assigns the maximum absolute z-score +of all the models of normalcy as the overall anomaly score.

+
+\[\begin{split}\begin{align*} +\text{Let } \space z_k(x_t) \text{ be the z-score of the } & k\text{-lag at } t, \space \Delta_k(x_t) +\text{ and } p \text{ be the lag inflation} \\ +& \\ +\text{the anomaly score } z(x_t) & = z_{k^*}(x_t) \\ +\text{where } k^* & = \text{argmax}_k \space | z_k(x_t) | / k^p +\end{align*}\end{split}\]
+
+
Parameters
+

config (DetectorConfig) – model configuration

+
+
+
+
+config_class
+

alias of ZMSConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+
+property n_lags
+
+ +
+
+property lag_scales: List[int]
+
+ +
+
+property lag_inflation
+
+ +
+
+property adjust_z_scores: bool
+
+ +
+
+train(train_data, train_config=None, anomaly_labels=None, post_rule_train_config=None)
+

Trains the anomaly detector (unsupervised) and its post-rule (supervised, if labels are given) on train data.

+
+
Parameters
+
    +

  • train_data (TimeSeries) – a TimeSeries of metric values to train the model.

    • +

  • train_config – Additional training configs, if needed. Only required for some models.

    • +

  • anomaly_labels (Optional[TimeSeries]) – a TimeSeries indicating which timestamps are anomalous. Optional.

    • +

  • post_rule_train_config – The config to use for training the model’s post-rule. The model’s default +post-rule train config is used if none is supplied here.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

A TimeSeries of the model’s anomaly scores on the training data.

+
+
+
+ +
+ +
+
+
+

Multivariate models

+
+

anomaly.isolation_forest

+

The classic isolation forest model for anomaly detection.

+
+
+class merlion.models.anomaly.isolation_forest.IsolationForestConfig(max_n_samples=None, n_estimators=100, n_jobs=-1, max_score=1000, threshold=None, enable_calibrator=True, enable_threshold=True, transform=None, **kwargs)
+

Bases: DetectorConfig

+

Configuration class for IsolationForest.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • max_n_samples (Optional[int]) – Maximum number of samples to allow the isolation +forest to train on. Specify None to use all samples in the +training data.

    • +

  • n_estimators (int) – number of trees in the isolation forest.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.isolation_forest.IsolationForest(config)
+

Bases: DetectorBase

+

The classic isolation forest algorithm, proposed in +Liu et al. 2008

+
+
Parameters
+

config (IsolationForestConfig) – model configuration

+
+
+
+
+config_class
+

alias of IsolationForestConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+ +
+
+

anomaly.random_cut_forest

+

Wrapper around AWS’s Random Cut Forest anomaly detection model.

+
+
+class merlion.models.anomaly.random_cut_forest.JVMSingleton
+

Bases: object

+
+
+classmethod gateway()
+
+ +
+ +
+
+class merlion.models.anomaly.random_cut_forest.RandomCutForestConfig(n_estimators=100, parallel=False, seed=None, max_n_samples=512, thread_pool_size=1, online_updates=False, max_score=1000, threshold=None, enable_calibrator=True, enable_threshold=True, transform=None, **kwargs)
+

Bases: DetectorConfig

+

Configuration class for RandomCutForest. Refer to +https://github.com/aws/random-cut-forest-by-aws/tree/main/Java for +further documentation and defaults of the Java class.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • n_estimators (int) – The number of trees in this forest.

    • +

  • parallel (bool) – If true, then the forest will create an internal thread +pool. Forest updates and traversals will be submitted to this thread +pool, and individual trees will be updated or traversed in parallel. +For larger shingle sizes, dimensions, and number of trees, +parallelization may improve throughput. +We recommend users benchmark against their target use case.

    • +

  • seed (Optional[int]) – the random seed

    • +

  • max_n_samples (int) – The number of samples retained by by stream +samplers in this forest.

    • +

  • thread_pool_size (int) – The number of threads to use in the internal +thread pool.

    • +

  • online_updates (bool) – Whether to update the model while running +using it to evaluate new data.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+
+property java_params
+
+ +
+ +
+
+class merlion.models.anomaly.random_cut_forest.RandomCutForest(config)
+

Bases: DetectorBase

+

The random cut forest is a refinement of the classic isolation forest +algorithm. It was proposed in Guha et al. 2016.

+
+
Parameters
+

config (RandomCutForestConfig) – model configuration

+
+
+
+
+config_class
+

alias of RandomCutForestConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+
+property online_updates: bool
+
+ +
+ +
+
+

anomaly.autoencoder

+

The autoencoder-based anomaly detector for multivariate time series

+
+
+class merlion.models.anomaly.autoencoder.AutoEncoderConfig(hidden_size=5, layer_sizes=(25, 10, 5), sequence_len=1, lr=0.001, batch_size=512, num_epochs=50, **kwargs)
+

Bases: DetectorConfig, NormalizingConfig

+

Configuration class for AutoEncoder. The normalization is inherited from NormalizingConfig. +The input data will be standardized automatically.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • hidden_size (int) – The latent size

    • +

  • layer_sizes (Sequence[int]) – The hidden layer sizes for the MLP encoder and decoder, +e.g., (25, 10, 5) for encoder and (5, 10, 25) for decoder

    • +

  • sequence_len (int) – The input series length, e.g., input = [x(t-sequence_len+1)…,x(t-1),x(t)]

    • +

  • lr (float) – The learning rate during training

    • +

  • batch_size (int) – The batch size during training

    • +

  • num_epochs (int) – The number of training epochs

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • normalize – Pre-trained normalization transformation (optional).

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.autoencoder.AutoEncoder(config)
+

Bases: DetectorBase

+

The autoencoder-based multivariate time series anomaly detector. +This detector utilizes an autoencoder to infer the correlations between +different time series and estimate the joint distribution of the variables +for anomaly detection.

+ +
+
Parameters
+

config (AutoEncoderConfig) – model configuration

+
+
+
+
+config_class
+

alias of AutoEncoderConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+ +
+
+

anomaly.vae

+

The VAE-based anomaly detector for multivariate time series

+
+
+class merlion.models.anomaly.vae.VAEConfig(encoder_hidden_sizes=(25, 10, 5), decoder_hidden_sizes=(5, 10, 25), latent_size=5, sequence_len=1, kld_weight=1.0, dropout_rate=0.0, num_eval_samples=10, lr=0.001, batch_size=1024, num_epochs=10, **kwargs)
+

Bases: DetectorConfig, NormalizingConfig

+

Configuration class for VAE. The normalization is inherited from NormalizingConfig. +The input data will be standardized automatically.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • encoder_hidden_sizes (Sequence[int]) – The hidden layer sizes of the MLP encoder

    • +

  • decoder_hidden_sizes (Sequence[int]) – The hidden layer sizes of the MLP decoder

    • +

  • latent_size (int) – The latent size

    • +

  • sequence_len (int) – The input series length, e.g., input = [x(t-sequence_len+1)…,x(t-1),x(t)]

    • +

  • kld_weight (float) – The regularization weight for the KL divergence term

    • +

  • dropout_rate (float) – The dropout rate for the encoder and decoder

    • +

  • num_eval_samples (int) – The number of sampled latent variables during prediction

    • +

  • lr (float) – The learning rate during training

    • +

  • batch_size (int) – The batch size during training

    • +

  • num_epochs (int) – The number of training epochs

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • normalize – Pre-trained normalization transformation (optional).

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.vae.VAE(config)
+

Bases: DetectorBase

+

The VAE-based multivariate time series anomaly detector. +This detector utilizes a variational autoencoder to infer the correlations between +different time series and estimate the distribution of the reconstruction errors +for anomaly detection.

+ +
+
Parameters
+

config (VAEConfig) – model configuration

+
+
+
+
+config_class
+

alias of VAEConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+ +
+
+

anomaly.dagmm

+

Deep autoencoding Gaussian mixture model for anomaly detection (DAGMM)

+
+
+class merlion.models.anomaly.dagmm.DAGMMConfig(gmm_k=3, hidden_size=5, sequence_len=1, lambda_energy=0.1, lambda_cov_diag=0.005, lr=0.001, batch_size=256, num_epochs=10, **kwargs)
+

Bases: DetectorConfig, NormalizingConfig

+

Configuration class for DAGMM. The normalization is inherited from NormalizingConfig. +The input data will be standardized automatically.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • gmm_k (int) – The number of Gaussian distributions

    • +

  • hidden_size (int) – The hidden size of the autoencoder module in DAGMM

    • +

  • sequence_len (int) – The input series length, e.g., input = [x(t-sequence_len+1)…,x(t-1),x(t)]

    • +

  • lambda_energy (float) – The regularization weight for the energy term

    • +

  • lambda_cov_diag (float) – The regularization weight for the covariance diagonal entries

    • +

  • lr (float) – The learning rate during training

    • +

  • batch_size (int) – The batch size during training

    • +

  • num_epochs (int) – The number of training epochs

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • normalize – Pre-trained normalization transformation (optional).

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.dagmm.DAGMM(config)
+

Bases: DetectorBase, MultipleTimeseriesDetectorMixin

+

Deep autoencoding Gaussian mixture model for anomaly detection (DAGMM). +DAGMM combines an autoencoder with a Gaussian mixture model to model the distribution +of the reconstruction errors. DAGMM jointly optimizes the parameters of the deep autoencoder +and the mixture model simultaneously in an end-to-end fashion.

+ +
+
+config_class
+

alias of DAGMMConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+
+train_multiple(multiple_train_data, train_config=None, anomaly_labels=None, post_rule_train_config=None)
+

Trains the anomaly detector (unsupervised) and its post-rule +(supervised, if labels are given) on the input multiple time series.

+
+
Parameters
+
    +

  • multiple_train_data (List[TimeSeries]) – a list of TimeSeries of metric values to train the model.

    • +

  • train_config –

    Additional training config dict with keys:

    +
      +
    • +
      ”n_epochs”: int indicating how many times the model must be
      +
      trained on the timeseries in multiple_train_data. Defaults to 1.
      +
      +
      • +
    • +
      ”shuffle”: bool indicating if the multiple_train_data collection
      +
      should be shuffled before every epoch. Defaults to True if “n_epochs” > 1.
      +
      +
      • +
    +

    • +

  • anomaly_labels (Optional[List[TimeSeries]]) – a list of TimeSeries indicating which timestamps are anomalous. Optional.

    • +

  • post_rule_train_config – The config to use for training the +model’s post-rule. The model’s default post-rule train config is +used if none is supplied here.

    • +
+
+
Return type
+

List[TimeSeries]

+
+
Returns
+

A list of TimeSeries of the model’s anomaly scores on the training +data with each element corresponds to time series from multiple_train_data.

+
+
+
+ +
+ +
+
+

anomaly.lstm_ed

+

The LSTM-encoder-decoder-based anomaly detector for multivariate time series

+
+
+class merlion.models.anomaly.lstm_ed.LSTMEDConfig(hidden_size=5, sequence_len=20, n_layers=(1, 1), dropout=(0, 0), lr=0.001, batch_size=256, num_epochs=10, **kwargs)
+

Bases: DetectorConfig, NormalizingConfig

+

Configuration class for LSTM-encoder-decoder. The normalization is inherited from NormalizingConfig. +The input data will be standardized automatically.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • hidden_size (int) – The hidden state size of the LSTM modules

    • +

  • sequence_len (int) – The input series length, e.g., input = [x(t-sequence_len+1)…,x(t-1),x(t)]

    • +

  • n_layers (Sequence[int]) – The number of layers for the LSTM encoder and decoder. n_layer has two values, i.e., +n_layer[0] is the number of encoder layers and n_layer[1] is the number of decoder layers.

    • +

  • dropout (Sequence[int]) – The dropout rate for the LSTM encoder and decoder. dropout has two values, i.e., +dropout[0] is the dropout rate for the encoder and dropout[1] is the dropout rate for the decoder.

    • +

  • lr (float) – The learning rate during training

    • +

  • batch_size (int) – The batch size during training

    • +

  • num_epochs (int) – The number of training epochs

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • normalize – Pre-trained normalization transformation (optional).

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.lstm_ed.LSTMED(config)
+

Bases: DetectorBase

+

The LSTM-encoder-decoder-based multivariate time series anomaly detector. +The time series representation is modeled by an encoder-decoder network where +both encoder and decoder are LSTMs. The distribution of the reconstruction error +is estimated for anomaly detection.

+
+
Parameters
+

config (LSTMEDConfig) – model configuration

+
+
+
+
+config_class
+

alias of LSTMEDConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+ +
+
+

anomaly.deep_point_anomaly_detector

+

Deep Point Anomaly Detector algorithm.

+
+
+class merlion.models.anomaly.deep_point_anomaly_detector.DeepPointAnomalyDetectorConfig(max_score=1000, threshold=None, enable_calibrator=True, enable_threshold=True, transform=None, **kwargs)
+

Bases: DetectorConfig

+

Config object used to define an anomaly detection model.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • max_score (float) – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+ +
+
+class merlion.models.anomaly.deep_point_anomaly_detector.DeepPointAnomalyDetector(config)
+

Bases: DetectorBase

+

Given a time series tuple (time, signal), this algorithm trains an MLP with +each element in time and corresponding signal as input-taget pair. Once the +MLP is trained for a few itertions, the loss values at each time is +regarded as the anomaly score for the corresponding signal. The intuition is +that DNNs learn global patterns before overfitting local details. Therefore +any point anomalies in the signal will have high MLP loss. These intuitions +can be found in: +Arpit, Devansh, et al. “A closer look at memorization in deep networks.” ICML 2017 +Rahaman, Nasim, et al. “On the spectral bias of neural networks.” ICML 2019

+
+
Parameters
+

config (DeepPointAnomalyDetectorConfig) – model configuration

+
+
+
+
+config_class
+

alias of DeepPointAnomalyDetectorConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+ +
+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
+ +
+
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.models.automl.html b/v2.0.2/merlion.models.automl.html new file mode 100644 index 000000000..9dc50e8ba --- /dev/null +++ b/v2.0.2/merlion.models.automl.html @@ -0,0 +1,1217 @@ + + + + + + automl — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ + + +
+

automl

+

Contains all AutoML model variants & some utilities.

+

Base classes:

+ ++++ + + + + + +

base

Base class/mixin for AutoML hyperparameter search.

+

Models:

+ ++++ + + + + + + + + + + + +

autoets

Automatic hyperparamter selection for ETS.

autoprophet

Automatic hyperparameter selection for Facebook's Prophet.

autosarima

Automatic hyperparameter selection for SARIMA.

+

Utilities:

+ ++++ + + + + + + + + +

seasonality

Automatic seasonality detection.

search

Abstractions for hyperparameter search.

+
+

Base classes

+
+

automl.base

+

Base class/mixin for AutoML hyperparameter search.

+
+
+class merlion.models.automl.base.AutoMLMixIn(config=None, model=None, **kwargs)
+

Bases: LayeredModel

+

Abstract base class which converts LayeredModel into an AutoML model.

+
+
+abstract generate_theta(train_data)
+
+
Parameters
+

train_data (TimeSeries) – Pre-processed training data to use for generation of hyperparameters \(\theta\)

+
+
Return type
+

Iterator

+
+
+

Returns an iterator of hyperparameter candidates for consideration with th underlying model.

+
+ +
+
+abstract evaluate_theta(thetas, train_data, train_config=None, exog_data=None)
+
+
Parameters
+
    +

  • thetas (Iterator) – Iterator of the hyperparameter candidates

    • +

  • train_data (TimeSeries) – Pre-processed training data

    • +

  • train_config – Training configuration

    • +
+
+
Return type
+

Tuple[Any, Optional[ModelBase], Optional[Tuple[TimeSeries, Optional[TimeSeries]]]]

+
+
+

Return the optimal hyperparameter, as well as optionally a model and result of the training procedure.

+
+ +
+
+abstract set_theta(model, theta, train_data=None)
+
+
Parameters
+
    +

  • model – Underlying base model to which the new theta is applied

    • +

  • theta – Hyperparameter to apply

    • +

  • train_data (Optional[TimeSeries]) – Pre-processed training data (Optional)

    • +
+
+
+

Sets the hyperparameter to the provided model. This is used to apply the \(\theta\) to the model, since +this behavior is custom to every model. Oftentimes in internal implementations, model is the optimal model.

+
+ +
+ +
+
+class merlion.models.automl.base.InformationCriterion(value)
+

Bases: Enum

+

An enumeration.

+
+
+AIC = 1
+

Akaike information criterion. Computed as

+
+\[\mathrm{AIC} = 2k - 2\mathrm{ln}(L)\]
+

where k is the number of parameters, and L is the model’s likelihood.

+
+ +
+
+BIC = 2
+

Bayesian information criterion. Computed as

+
+\[k \mathrm{ln}(n) - 2 \mathrm{ln}(L)\]
+

where n is the sample size, k is the number of parameters, and L is the model’s likelihood.

+
+ +
+
+AICc = 3
+

Akaike information criterion with correction for small sample size. Computed as

+
+\[\mathrm{AICc} = \mathrm{AIC} + \frac{2k^2 + 2k}{n - k - 1}\]
+

where n is the sample size, and k is the number of paramters.

+
+ +
+ +
+
+class merlion.models.automl.base.ICConfig(information_criterion=InformationCriterion.AIC, transform=None, **kwargs)
+

Bases: Config

+

Mix-in to add an information criterion parameter to a model config.

+
+
Parameters
+
    +

  • information_criterion (InformationCriterion) – information criterion to select the best model.

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+
+property information_criterion
+
+ +
+ +
+
+class merlion.models.automl.base.ICAutoMLForecaster(config=None, model=None, **kwargs)
+

Bases: AutoMLMixIn, ForecasterBase

+

AutoML model which uses an information criterion to determine which model paramters are best.

+
+
+config_class
+

alias of ICConfig

+
+ +
+
+property information_criterion
+
+ +
+
+abstract get_ic(model, train_data, train_result)
+

Returns the information criterion of the model based on the given training data & the model’s train result.

+
+
Parameters
+
    +

  • model – One of the models being tried. Must be trained.

    • +

  • train_data (DataFrame) – The target sequence of the training data as a pandas.DataFrame.

    • +

  • train_result (Tuple[DataFrame, Optional[DataFrame]]) – The result of calling model._train().

    • +
+
+
Return type
+

float

+
+
Returns
+

The information criterion evaluating the model’s goodness of fit.

+
+
+
+ +
+
+evaluate_theta(thetas, train_data, train_config=None, exog_data=None)
+
+
Parameters
+
    +

  • thetas (Iterator) – Iterator of the hyperparameter candidates

    • +

  • train_data (TimeSeries) – Pre-processed training data

    • +

  • train_config – Training configuration

    • +
+
+
Return type
+

Tuple[Any, ModelBase, Tuple[TimeSeries, Optional[TimeSeries]]]

+
+
+

Return the optimal hyperparameter, as well as optionally a model and result of the training procedure.

+
+ +
+ +
+
+
+

Models

+
+

automl.autoets

+

Automatic hyperparamter selection for ETS.

+
+
+class merlion.models.automl.autoets.AutoETSConfig(model=None, auto_seasonality=True, auto_error=True, auto_trend=True, auto_seasonal=True, auto_damped=True, periodicity_strategy=PeriodicityStrategy.ACF, information_criterion=InformationCriterion.AIC, additive_only=False, allow_multiplicative_trend=False, restrict=True, pval=0.05, max_lag=None, model_kwargs=None, transform=None, **kwargs)
+

Bases: SeasonalityConfig, ICConfig

+

Configuration class for AutoETS. Act as a wrapper around a ETS model, which automatically detects +the hyperparameters seasonal_periods, error, trend, damped_trend and seasonal.

+
+
Parameters
+
    +

  • model (Union[ETS, dict, None]) – The model being wrapped, or a dict representing it.

    • +

  • auto_seasonality (bool) – Whether to automatically detect the seasonality.

    • +

  • auto_error (bool) – Whether to automatically detect the error components.

    • +

  • auto_trend (bool) – Whether to automatically detect the trend components.

    • +

  • auto_seasonal (bool) – Whether to automatically detect the seasonal components.

    • +

  • auto_damped (bool) – Whether to automatically detect the damped trend components.

    • +

  • periodicity_strategy (PeriodicityStrategy) – Strategy to choose the seasonality if multiple candidates are detected.

    • +

  • information_criterion (InformationCriterion) – information criterion to select the best model.

    • +

  • additive_only (bool) – If True, the search space will only consider additive models.

    • +

  • allow_multiplicative_trend (bool) – If True, models with multiplicative trend are allowed in the search space.

    • +

  • restrict (bool) – If True, the models with infinite variance will not be allowed in the search space.

    • +

  • pval – p-value for deciding whether a detected seasonality is statistically significant.

    • +

  • max_lag – max lag considered for seasonality detection.

    • +

  • model_kwargs – Keyword arguments used specifically to initialize the underlying model. Only used if +model is a dict. Will override keys in the model dict if specified.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • kwargs – Any other keyword arguments (e.g. for initializing a base class). If model is a dict, +we will also try to pass these arguments when creating the actual underlying model. However, they will +not override arguments in either the model dict or model_kwargs dict.

    • +
+
+
+
+ +
+
+class merlion.models.automl.autoets.AutoETS(config)
+

Bases: ICAutoMLForecaster, SeasonalityLayer

+

Wrapper around a ETS model, which automatically detects +the hyperparameters seasonal_periods, error, trend, damped_trend and seasonal.

+
+
+config_class
+

alias of AutoETSConfig

+
+ +
+
+generate_theta(train_data)
+

generate [theta]. theta is a list of parameter combination [error, trend, damped_trend, seasonal]

+
+
Return type
+

Iterator

+
+
+
+ +
+
+set_theta(model, theta, train_data=None)
+
+
Parameters
+
    +

  • model – Underlying base model to which the new theta is applied

    • +

  • theta – Hyperparameter to apply

    • +

  • train_data (Optional[TimeSeries]) – Pre-processed training data (Optional)

    • +
+
+
+

Sets the hyperparameter to the provided model. This is used to apply the \(\theta\) to the model, since +this behavior is custom to every model. Oftentimes in internal implementations, model is the optimal model.

+
+ +
+
+get_ic(model, train_data, train_result)
+

Returns the information criterion of the model based on the given training data & the model’s train result.

+
+
Parameters
+
    +

  • model – One of the models being tried. Must be trained.

    • +

  • train_data (DataFrame) – The target sequence of the training data as a pandas.DataFrame.

    • +

  • train_result (Tuple[DataFrame, DataFrame]) – The result of calling model._train().

    • +
+
+
Return type
+

float

+
+
Returns
+

The information criterion evaluating the model’s goodness of fit.

+
+
+
+ +
+ +
+
+

automl.autoprophet

+

Automatic hyperparameter selection for Facebook’s Prophet.

+
+
+class merlion.models.automl.autoprophet.AutoProphetConfig(model=None, periodicity_strategy=PeriodicityStrategy.All, information_criterion=InformationCriterion.AIC, pval=0.05, max_lag=None, model_kwargs=None, transform=None, **kwargs)
+

Bases: SeasonalityConfig, ICConfig

+

Config class for Prophet with automatic seasonality detection & other hyperparameter selection.

+
+
Parameters
+
    +

  • model (Union[Prophet, dict, None]) – The model being wrapped, or a dict representing it.

    • +

  • periodicity_strategy (Union[PeriodicityStrategy, str]) – Strategy to choose the seasonality if multiple candidates are detected.

    • +

  • information_criterion (InformationCriterion) – information criterion to select the best model.

    • +

  • pval – p-value for deciding whether a detected seasonality is statistically significant.

    • +

  • max_lag – max lag considered for seasonality detection.

    • +

  • model_kwargs – Keyword arguments used specifically to initialize the underlying model. Only used if +model is a dict. Will override keys in the model dict if specified.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • kwargs – Any other keyword arguments (e.g. for initializing a base class). If model is a dict, +we will also try to pass these arguments when creating the actual underlying model. However, they will +not override arguments in either the model dict or model_kwargs dict.

    • +
+
+
+
+
+property multi_seasonality
+
+
Returns
+

True because Prophet supports multiple seasonality.

+
+
+
+ +
+ +
+
+class merlion.models.automl.autoprophet.AutoProphet(config=None, model=None, **kwargs)
+

Bases: ICAutoMLForecaster, SeasonalityLayer

+

Prophet with automatic seasonality detection. Automatically detects and adds +additional seasonalities that the existing Prophet may not detect (e.g. hourly). +Also automatically chooses other hyperparameters.

+
+
+config_class
+

alias of AutoProphetConfig

+
+ +
+
+property supports_exog
+

Whether the model supports exogenous regressors.

+
+ +
+
+generate_theta(train_data)
+
+
Parameters
+

train_data (TimeSeries) – Pre-processed training data to use for generation of hyperparameters \(\theta\)

+
+
Return type
+

Iterator

+
+
+

Returns an iterator of hyperparameter candidates for consideration with th underlying model.

+
+ +
+
+set_theta(model, theta, train_data=None)
+
+
Parameters
+
    +

  • model – Underlying base model to which the new theta is applied

    • +

  • theta – Hyperparameter to apply

    • +

  • train_data (Optional[TimeSeries]) – Pre-processed training data (Optional)

    • +
+
+
+

Sets the hyperparameter to the provided model. This is used to apply the \(\theta\) to the model, since +this behavior is custom to every model. Oftentimes in internal implementations, model is the optimal model.

+
+ +
+
+get_ic(model, train_data, train_result)
+

Returns the information criterion of the model based on the given training data & the model’s train result.

+
+
Parameters
+
    +

  • model – One of the models being tried. Must be trained.

    • +

  • train_data (DataFrame) – The target sequence of the training data as a pandas.DataFrame.

    • +

  • train_result (Tuple[DataFrame, DataFrame]) – The result of calling model._train().

    • +
+
+
Return type
+

float

+
+
Returns
+

The information criterion evaluating the model’s goodness of fit.

+
+
+
+ +
+ +
+
+

automl.autosarima

+

Automatic hyperparameter selection for SARIMA.

+
+
+class merlion.models.automl.autosarima.AutoSarimaConfig(model=None, auto_seasonality=True, periodicity_strategy=PeriodicityStrategy.ACF, auto_pqPQ=True, auto_d=True, auto_D=True, maxiter=None, max_k=100, max_dur=3600, approximation=None, approx_iter=None, pval=0.05, max_lag=None, model_kwargs=None, transform=None, **kwargs)
+

Bases: SeasonalityConfig

+

Configuration class for AutoSarima. Acts as a wrapper around a Sarima model, which automatically detects +the seasonality, (seasonal) differencing order, and (seasonal) AR/MA orders. If a non-numeric value is specified +for any of the relevant parameters in the order or seasonal order, we assume that the user wishes to detect that +parameter automatically.

+
+

Note

+

The automatic selection of AR, MA, seasonal AR, and seasonal MA parameters is implemented in a coupled way. +The user must specify all of these parameters explicitly to avoid automatic selection.

+
+
+
Parameters
+
    +

  • model (Union[Sarima, dict, None]) – The model being wrapped, or a dict representing it.

    • +

  • auto_seasonality (bool) – Whether to automatically detect the seasonality.

    • +

  • periodicity_strategy (PeriodicityStrategy) – Periodicity Detection Strategy.

    • +

  • auto_pqPQ (bool) – Whether to automatically choose AR/MA orders p, q and seasonal AR/MA orders P, Q.

    • +

  • auto_d (bool) – Whether to automatically choose the difference order d.

    • +

  • auto_D (bool) – Whether to automatically choose the seasonal difference order D.

    • +

  • maxiter (Optional[int]) – The maximum number of iterations to perform

    • +

  • max_k (int) – Maximum number of models considered in the stepwise search

    • +

  • max_dur (float) – Maximum training time considered in the stepwise search

    • +

  • approximation (Optional[bool]) – Whether to use approx_iter iterations (instead +of maxiter) to speed up computation. If None, we use +approximation mode when the training data is too long (>150), or when +the length off the period is too high (periodicity > 12).

    • +

  • approx_iter (Optional[int]) – The number of iterations to perform in approximation mode

    • +

  • pval – p-value for deciding whether a detected seasonality is statistically significant.

    • +

  • max_lag – max lag considered for seasonality detection.

    • +

  • model_kwargs – Keyword arguments used specifically to initialize the underlying model. Only used if +model is a dict. Will override keys in the model dict if specified.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • kwargs – Any other keyword arguments (e.g. for initializing a base class). If model is a dict, +we will also try to pass these arguments when creating the actual underlying model. However, they will +not override arguments in either the model dict or model_kwargs dict.

    • +
+
+
+
+
+property order
+
+ +
+
+property seasonal_order
+
+ +
+ +
+
+class merlion.models.automl.autosarima.AutoSarima(config=None, model=None, **kwargs)
+

Bases: SeasonalityLayer

+
+
+config_class
+

alias of AutoSarimaConfig

+
+ +
+
+property supports_exog
+

Whether the model supports exogenous regressors.

+
+ +
+
+generate_theta(train_data)
+

generate [action, theta]. action is an indicator for stepwise seach (stepwsie) of +p, q, P, Q, trend parameters or use a predefined parameter combination (pqPQ) +theta is a list of parameter combination [order, seasonal_order, trend]

+
+
Return type
+

Iterator

+
+
+
+ +
+
+evaluate_theta(thetas, train_data, train_config=None, exog_data=None)
+
+
Parameters
+
    +

  • thetas (Iterator) – Iterator of the hyperparameter candidates

    • +

  • train_data (TimeSeries) – Pre-processed training data

    • +

  • train_config – Training configuration

    • +
+
+
Return type
+

Tuple[Any, Optional[Sarima], Optional[Tuple[TimeSeries, Optional[TimeSeries]]]]

+
+
+

Return the optimal hyperparameter, as well as optionally a model and result of the training procedure.

+
+ +
+
+set_theta(model, theta, train_data=None)
+
+
Parameters
+
    +

  • model – Underlying base model to which the new theta is applied

    • +

  • theta – Hyperparameter to apply

    • +

  • train_data (Optional[TimeSeries]) – Pre-processed training data (Optional)

    • +
+
+
+

Sets the hyperparameter to the provided model. This is used to apply the \(\theta\) to the model, since +this behavior is custom to every model. Oftentimes in internal implementations, model is the optimal model.

+
+ +
+ +
+
+
+

Utilities

+
+

automl.seasonality

+

Automatic seasonality detection. +Note that the static method merlion.models.automl.seasonality.SeasonalityLayer.detect_seasonality() +can be used to find the seasonality of an arbitrary numpy.array, without needing to initialize a model.

+
+
+class merlion.models.automl.seasonality.PeriodicityStrategy(value)
+

Bases: Enum

+

Strategy to choose the seasonality if multiple candidates are detected.

+
+
+ACF = 1
+

Select the seasonality value with the highest autocorrelation.

+
+ +
+
+Min = 2
+

Select the minimum seasonality.

+
+ +
+
+Max = 3
+

Select the maximum seasonality.

+
+ +
+
+All = 4
+

Use all seasonalities. Only valid for models which support multiple seasonalities.

+
+ +
+ +
+
+class merlion.models.automl.seasonality.SeasonalityModel
+

Bases: object

+

Class provides simple implementation to set the seasonality in a model. Extend this class to implement custom +behavior for seasonality processing.

+
+
+abstract set_seasonality(theta, train_data)
+

Implement this method to do any model-specific adjustments on the seasonality that was provided by +SeasonalityLayer.

+
+
Parameters
+
    +

  • theta – Seasonality processed by SeasonalityLayer.

    • +

  • train_data (UnivariateTimeSeries) – Training data (or numpy array representing the target univariate) +for any model-specific adjustments you might want to make.

    • +
+
+
+
+ +
+ +
+
+class merlion.models.automl.seasonality.SeasonalityConfig(model, periodicity_strategy=PeriodicityStrategy.ACF, pval=0.05, max_lag=None, model_kwargs=None, transform=None, **kwargs)
+

Bases: LayeredModelConfig

+

Config object for an automatic seasonality detection layer.

+
+
Parameters
+
    +

  • model – The model being wrapped, or a dict representing it.

    • +

  • periodicity_strategy – Strategy to choose the seasonality if multiple candidates are detected.

    • +

  • pval (float) – p-value for deciding whether a detected seasonality is statistically significant.

    • +

  • max_lag (Optional[int]) – max lag considered for seasonality detection.

    • +

  • model_kwargs – Keyword arguments used specifically to initialize the underlying model. Only used if +model is a dict. Will override keys in the model dict if specified.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • kwargs – Any other keyword arguments (e.g. for initializing a base class). If model is a dict, +we will also try to pass these arguments when creating the actual underlying model. However, they will +not override arguments in either the model dict or model_kwargs dict.

    • +
+
+
+
+
+property multi_seasonality
+
+
Returns
+

Whether the model supports multiple seasonalities. False unless explicitly overridden.

+
+
+
+ +
+
+property periodicity_strategy: PeriodicityStrategy
+
+
Returns
+

Strategy to choose the seasonality if multiple candidates are detected.

+
+
+
+ +
+ +
+
+class merlion.models.automl.seasonality.SeasonalityLayer(config=None, model=None, **kwargs)
+

Bases: AutoMLMixIn

+

Seasonality Layer that uses automatically determines the seasonality of your data. Can be used directly on +any model that implements SeasonalityModel class. The algorithmic idea is from the +theta method. We find a set of +multiple candidate seasonalites, and we return the best one(s) based on the PeriodicityStrategy.

+
+
+config_class
+

alias of SeasonalityConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate
+

Whether the model only works with univariate time series.

+
+ +
+
+property multi_seasonality
+
+
Returns
+

Whether the model supports multiple seasonalities.

+
+
+
+ +
+
+property periodicity_strategy
+
+
Returns
+

Strategy to choose the seasonality if multiple candidates are detected.

+
+
+
+ +
+
+property pval
+
+
Returns
+

p-value for deciding whether a detected seasonality is statistically significant.

+
+
+
+ +
+
+property max_lag
+
+
Returns
+

max_lag for seasonality detection

+
+
+
+ +
+
+static detect_seasonality(x, max_lag=None, pval=0.05, periodicity_strategy=PeriodicityStrategy.ACF)
+

Helper method to detect the seasonality of a time series.

+
+
Parameters
+
    +

  • x (array) – The numpy array of values whose seasonality we want to detect. Must be univariate & flattened.

    • +

  • periodicity_strategy (PeriodicityStrategy) – Strategy to choose the seasonality if multiple candidates are detected.

    • +

  • pval (float) – p-value for deciding whether a detected seasonality is statistically significant.

    • +

  • max_lag (Optional[int]) – max lag considered for seasonality detection.

    • +
+
+
Return type
+

List[int]

+
+
+
+ +
+
+set_theta(model, theta, train_data=None)
+
+
Parameters
+
    +

  • model – Underlying base model to which the new theta is applied

    • +

  • theta – Hyperparameter to apply

    • +

  • train_data (Optional[TimeSeries]) – Pre-processed training data (Optional)

    • +
+
+
+

Sets the hyperparameter to the provided model. This is used to apply the \(\theta\) to the model, since +this behavior is custom to every model. Oftentimes in internal implementations, model is the optimal model.

+
+ +
+
+evaluate_theta(thetas, train_data, train_config=None, exog_data=None)
+
+
Parameters
+
    +

  • thetas (Iterator) – Iterator of the hyperparameter candidates

    • +

  • train_data (TimeSeries) – Pre-processed training data

    • +

  • train_config – Training configuration

    • +
+
+
Return type
+

Tuple[Any, Optional[ModelBase], Optional[Tuple[TimeSeries, Optional[TimeSeries]]]]

+
+
+

Return the optimal hyperparameter, as well as optionally a model and result of the training procedure.

+
+ +
+
+generate_theta(train_data)
+
+
Parameters
+

train_data (TimeSeries) – Pre-processed training data to use for generation of hyperparameters \(\theta\)

+
+
Return type
+

Iterator

+
+
+

Returns an iterator of hyperparameter candidates for consideration with th underlying model.

+
+ +
+ +
+
+

automl.search

+

Abstractions for hyperparameter search.

+
+
+class merlion.models.automl.search.GridSearch(param_values, restrictions=None)
+

Bases: object

+

Iterator over a grid of parameter values, skipping any restricted combinations of values.

+
+
Parameters
+
    +

  • param_values (Dict[str, List]) – a dict mapping a set of parameter names to lists of values they can take on.

    • +

  • restrictions (Optional[List[Dict[str, Any]]]) – a list of dicts indicating inadmissible combinations of parameter values. +For example, an ETS model has parameters error (add/mul), trend (add/mul/none), seasonal (add/mul), +and damped_trend (True/False). If we are only considering additive models, we would impose the restrictions +[{"error": "mul"}, {"trend": "mul"}, {"seasonal": "mul"}]. Since a damped trend is only possible if +the model has a trend, we would add the restriction {"trend": None, "damped_trend": True}.

    • +
+
+
+
+ +
+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
+ +
+
Versions
+ + + +
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+ + + + +
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+ + + + +
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+ + + + +
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+ + + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.models.ensemble.html b/v2.0.2/merlion.models.ensemble.html new file mode 100644 index 000000000..bff1b03e3 --- /dev/null +++ b/v2.0.2/merlion.models.ensemble.html @@ -0,0 +1,989 @@ + + + + + + ensemble — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ + + +
+

ensemble

+

Ensembles of models and automated model selection.

+ ++++ + + + + + + + + + + + + + + +

base

Base class for ensembles of models.

combine

Rules for combining the outputs of multiple time series models.

anomaly

Ensembles of anomaly detectors.

forecast

Ensembles of forecasters.

+
+

ensemble.base

+

Base class for ensembles of models.

+
+
+class merlion.models.ensemble.base.EnsembleConfig(models=None, combiner=None, transform=None, **kwargs)
+

Bases: Config

+

An ensemble config contains the each individual model in the ensemble, as well as the Combiner object +to combine those models’ outputs. The rationale behind placing the model objects in the EnsembleConfig +(rather than in the Ensemble itself) is discussed in more detail in the documentation for LayeredModel.

+
+
Parameters
+
    +

  • models (Optional[List[Union[ModelBase, Dict]]]) – A list of models or dicts representing them.

    • +

  • combiner (Optional[CombinerBase]) – The CombinerBase object to combine the outputs of the models in the ensemble.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • kwargs – Any additional kwargs for Config

    • +
+
+
+
+
+models: List[ModelBase]
+
+ +
+
+to_dict(_skipped_keys=None)
+
+
Returns
+

dict with keyword arguments used to initialize the config class.

+
+
+
+ +
+ +
+
+class merlion.models.ensemble.base.EnsembleTrainConfig(valid_frac, per_model_train_configs=None)
+

Bases: object

+

Config object describing how to train an ensemble.

+
+
Parameters
+
    +

  • valid_frac – fraction of training data to use for validation.

    • +

  • per_model_train_configs – list of train configs to use for +individual models, one per model. None means that you use +the default for all models. Specifying None for an individual +model means that you use the default for that model.

    • +
+
+
+
+ +
+
+class merlion.models.ensemble.base.EnsembleBase(config=None, models=None)
+

Bases: ModelBase

+

An abstract class representing an ensemble of multiple models.

+
+
Parameters
+
    +

  • config (Optional[EnsembleConfig]) – The ensemble’s config

    • +

  • models (Optional[List[ModelBase]]) – The models in the ensemble. Only provide this argument if you did not specify config.models.

    • +
+
+
+
+
+config_class
+

alias of EnsembleConfig

+
+ +
+
+property models
+
+ +
+
+property combiner: CombinerBase
+
+
Returns
+

the object used to combine model outputs.

+
+
+
+ +
+
+reset()
+

Resets the model’s internal state.

+
+ +
+
+property models_used
+
+ +
+
+train_valid_split(transformed_train_data, train_config)
+
+
Return type
+

Tuple[TimeSeries, Optional[TimeSeries]]

+
+
+
+ +
+
+get_max_common_horizon(train_data=None)
+
+ +
+
+train_combiner(all_model_outs, target, **kwargs)
+
+
Return type
+

TimeSeries

+
+
+
+ +
+
+save(dirname, save_only_used_models=False, **save_config)
+

Saves the ensemble of models.

+
+
Parameters
+
    +

  • dirname (str) – directory to save the ensemble to

    • +

  • save_only_used_models – whether to save only the models that are actually used by the ensemble.

    • +

  • save_config – additional save config arguments

    • +
+
+
+
+ +
+
+to_bytes(save_only_used_models=False, **save_config)
+

Converts the entire model state and configuration to a single byte object.

+
+
Parameters
+
    +

  • save_only_used_models – whether to save only the models that are actually used by the ensemble.

    • +

  • save_config – additional configurations (if needed)

    • +
+
+
+
+ +
+ +
+
+

ensemble.combine

+

Rules for combining the outputs of multiple time series models.

+
+
+class merlion.models.ensemble.combine.CombinerBase(abs_score=False)
+

Bases: object

+

Abstract base class for combining the outputs of multiple models. Subclasses +should implement the abstract method _combine_univariates. All combiners +are callable objects.

+
+
+__call__(all_model_outs, target, _check_dim=True)
+

Applies the model combination rule to combine multiple model outputs.

+
+
Parameters
+
    +

  • all_model_outs (List[TimeSeries]) – a list of time series, with each time series +representing the output of a single model.

    • +

  • target (TimeSeries) – a target time series (e.g. labels)

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

a single time series of combined model outputs on this training data.

+
+
+
+ +
+
Parameters
+

abs_score – whether to take the absolute value of the model +outputs. Useful for anomaly detection.

+
+
+
+
+reset()
+
+ +
+
+property requires_training
+
+ +
+
+to_dict(_skipped_keys=None)
+
+ +
+
+classmethod from_dict(state)
+
+ +
+
+set_model_used(i, used)
+
+ +
+
+get_model_used(i)
+
+ +
+
+property models_used: List[bool]
+
+
Returns
+

which models are actually used to make predictions.

+
+
+
+ +
+
+train(all_model_outs, target=None, **kwargs)
+

Trains the model combination rule.

+
+
Parameters
+
    +

  • all_model_outs (List[TimeSeries]) – a list of time series, with each time series +representing the output of a single model.

    • +

  • target (Optional[TimeSeries]) – a target time series (e.g. labels)

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

a single time series of combined model outputs on this training data.

+
+
+
+ +
+ +
+
+class merlion.models.ensemble.combine.Mean(abs_score=False)
+

Bases: CombinerBase

+

Combines multiple models by taking their mean prediction.

+
+
Parameters
+

abs_score – whether to take the absolute value of the model +outputs. Useful for anomaly detection.

+
+
+
+
+property weights: ndarray
+
+ +
+ +
+
+class merlion.models.ensemble.combine.Median(abs_score=False)
+

Bases: CombinerBase

+

Combines multiple models by taking their median prediction.

+
+
Parameters
+

abs_score – whether to take the absolute value of the model +outputs. Useful for anomaly detection.

+
+
+
+ +
+
+class merlion.models.ensemble.combine.Max(abs_score=False)
+

Bases: CombinerBase

+

Combines multiple models by taking their max prediction.

+
+
Parameters
+

abs_score – whether to take the absolute value of the model +outputs. Useful for anomaly detection.

+
+
+
+ +
+
+class merlion.models.ensemble.combine.ModelSelector(metric, abs_score=False)
+

Bases: Mean

+

Takes the mean of the best models, where the models are ranked according to +the value of an evaluation metric.

+
+
Parameters
+
    +

  • metric (Union[str, TSADMetric, ForecastMetric]) – the evaluation metric to use

    • +

  • abs_score – whether to take the absolute value of the model +outputs. Useful for anomaly detection.

    • +
+
+
+
+
+property invert
+
+ +
+
+property requires_training
+
+ +
+
+to_dict(_skipped_keys=None)
+
+ +
+
+classmethod from_dict(state)
+
+ +
+
+train(all_model_outs, target=None, **kwargs)
+

Trains the model combination rule.

+
+
Parameters
+
    +

  • all_model_outs (List[TimeSeries]) – a list of time series, with each time series +representing the output of a single model.

    • +

  • target (Optional[TimeSeries]) – a target time series (e.g. labels)

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

a single time series of combined model outputs on this training data.

+
+
+
+ +
+ +
+
+class merlion.models.ensemble.combine.MetricWeightedMean(metric, abs_score=False)
+

Bases: ModelSelector

+

Computes a weighted average of model outputs with weights proportional to +the metric values (or their inverses).

+
+
Parameters
+
    +

  • metric (Union[str, TSADMetric, ForecastMetric]) – the evaluation metric to use

    • +

  • abs_score – whether to take the absolute value of the model +outputs. Useful for anomaly detection.

    • +
+
+
+
+
+property weights: ndarray
+
+ +
+ +
+
+class merlion.models.ensemble.combine.CombinerFactory
+

Bases: object

+

Factory object for creating combiner objects.

+
+
+classmethod create(name, **kwargs)
+
+
Return type
+

CombinerBase

+
+
+
+ +
+ +
+
+

ensemble.anomaly

+

Ensembles of anomaly detectors.

+
+
+class merlion.models.ensemble.anomaly.DetectorEnsembleConfig(enable_calibrator=False, max_score: float = 1000, threshold=None, enable_threshold=True, transform: TransformBase = None, models: List[Union[ModelBase, Dict]] = None, combiner: CombinerBase = None, **kwargs)
+

Bases: DetectorConfig, EnsembleConfig

+

Config class for an ensemble of anomaly detectors.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • enable_calibrator – Whether to enable calibration of the ensemble +anomaly score. False by default.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • models – A list of models or dicts representing them.

    • +

  • combiner – The CombinerBase object to combine the outputs of the models in the ensemble.

    • +

  • kwargs – Any additional kwargs for EnsembleConfig or DetectorConfig

    • +
+
+
+
+
+property per_model_threshold
+
+
Returns
+

whether to apply the thresholding rules of each individual +model, before combining their outputs. Only done if doing model +selection.

+
+
+
+ +
+ +
+
+class merlion.models.ensemble.anomaly.DetectorEnsembleTrainConfig(valid_frac=0.0, per_model_train_configs=None, per_model_post_rule_train_configs=None)
+

Bases: EnsembleTrainConfig

+

Config object describing how to train an ensemble of anomaly detectors.

+
+
Parameters
+
    +

  • valid_frac – fraction of training data to use for validation.

    • +

  • per_model_train_configs – list of train configs to use for individual models, one per model. +None means that you use the default for all models. Specifying None for an individual +model means that you use the default for that model.

    • +

  • per_model_post_rule_train_configs – list of post-rule train configs to use for individual models, one per +model. None means that you use the default for all models. Specifying None for an individual +model means that you use the default for that model.

    • +
+
+
+
+ +
+
+class merlion.models.ensemble.anomaly.DetectorEnsemble(config=None, models=None)
+

Bases: EnsembleBase, DetectorBase

+

Class representing an ensemble of multiple anomaly detection models.

+
+
Parameters
+

config (Optional[DetectorEnsembleConfig]) – model configuration

+
+
+
+
+config_class
+

alias of DetectorEnsembleConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+
+property per_model_threshold
+
+
Returns
+

whether to apply the threshold rule of each individual model +before aggregating their anomaly scores.

+
+
+
+ +
+ +
+
+

ensemble.forecast

+

Ensembles of forecasters.

+
+
+class merlion.models.ensemble.forecast.ForecasterEnsembleConfig(max_forecast_steps=None, target_seq_index=None, verbose=False, exog_transform: TransformBase = None, exog_aggregation_policy: Union[AggregationPolicy, str] = 'Mean', exog_missing_value_policy: Union[MissingValuePolicy, str] = 'ZFill', invert_transform=None, transform: TransformBase = None, models: List[Union[ModelBase, Dict]] = None, combiner: CombinerBase = None, **kwargs)
+

Bases: ForecasterExogConfig, EnsembleConfig

+

Config class for an ensemble of forecasters.

+
+
Parameters
+
    +

  • max_forecast_steps – Max # of steps we would like to forecast for. Required for some models like MSES.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • exog_transform – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • models – A list of models or dicts representing them.

    • +

  • combiner – The CombinerBase object to combine the outputs of the models in the ensemble.

    • +

  • kwargs – Any additional kwargs for Config

    • +
+
+
+
+
+property target_seq_index
+
+ +
+ +
+
+class merlion.models.ensemble.forecast.ForecasterEnsemble(config=None, models=None)
+

Bases: EnsembleBase, ForecasterExogBase

+

Class representing an ensemble of multiple forecasting models.

+
+
Parameters
+
    +

  • config (Optional[ForecasterEnsembleConfig]) – The ensemble’s config

    • +

  • models (Optional[List[ForecasterBase]]) – The models in the ensemble. Only provide this argument if you did not specify config.models.

    • +
+
+
+
+
+config_class
+

alias of ForecasterEnsembleConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+train_pre_process(train_data, exog_data=None, return_exog=None)
+

Applies pre-processing steps common for training most models.

+
+
Parameters
+

train_data (TimeSeries) – the original time series of training data

+
+
Return type
+

Union[TimeSeries, Tuple[TimeSeries, Optional[TimeSeries]]]

+
+
Returns
+

the training data, after any necessary pre-processing has been applied

+
+
+
+ +
+
+resample_time_stamps(time_stamps, time_series_prev=None)
+
+ +
+
+train_combiner(all_model_outs, target, **kwargs)
+
+
Return type
+

TimeSeries

+
+
+
+ +
+ +
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.models.forecast.html b/v2.0.2/merlion.models.forecast.html new file mode 100644 index 000000000..482829573 --- /dev/null +++ b/v2.0.2/merlion.models.forecast.html @@ -0,0 +1,2913 @@ + + + + + + forecast — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ + + +
+

forecast

+

Contains all forecasting models, including those which support +exogenous regressors.

+

For forecasting, we define an abstract base ForecasterBase class which inherits from ModelBase and supports the +following interface, in addition to model.save() and ForecasterClass.load defined for ModelBase:

+
    +

  1. model = ForecasterClass(config)

    +
    +
      +

    • initialization with a model-specific config (which inherits from ForecasterConfig)

      • +

    • configs contain:

      +
        +

      • a (potentially trainable) data pre-processing transform from merlion.transform; +note that model.transform is a property which refers to model.config.transform

        • +

      • model-specific hyperparameters

        • +

      • optionally, a maximum number of steps the model can forecast for

        • +
      +
      • +
    +
    +
    2. +

  2. model.forecast(time_stamps, time_series_prev=None)

    +
    +
      +

    • returns the forecast (TimeSeries) for future values at the time stamps specified by time_stamps, +as well as the standard error of that forecast (TimeSeries, may be None)

      • +

    • if time_series_prev is specified, it is used as the most recent context. Otherwise, the training data is used

      • +
    +
    +
    3. +

  3. model.train(train_data, train_config=None)

    +
      +

    • trains the model on the TimeSeries train_data

      • +

    • train_config (optional): extra configuration describing how the model should be trained. +Not used for all models. Class-level default provided for models which do use it.

      • +

    • returns the model’s prediction train_data, in the same format as if you called ForecasterBase.forecast +on the time stamps of train_data

      • +
    +
    4. +
+

Base classes:

+ ++++ + + + + + + + + + + + +

base

Base class for forecasting models.

deep_base

Base class for Deep Learning Forecasting Models

sklearn_base

Base class for forecasters which use arbitrary sklearn regression models internally.

+

Univariate models:

+ ++++ + + + + + + + + + + + + + + + + + +

arima

The classic statistical forecasting model ARIMA (AutoRegressive Integrated Moving Average).

sarima

A variant of ARIMA with a user-specified Seasonality.

ets

ETS (Error, Trend, Seasonal) forecasting model.

prophet

Wrapper around Facebook's popular Prophet model for time series forecasting.

smoother

Multi-Scale Exponential Smoother for univariate time series forecasting.

+

Multivariate models:

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + +

vector_ar

Vector AutoRegressive model for multivariate time series forecasting.

trees

Tree-based models for multivariate time series forecasting.

deep_ar

Implementation of Deep AR

autoformer

Implementation of Autoformer.

etsformer

Implementation of ETSformer.

informer

Implementation of Informer.

transformer

Implementation of Transformer for time series data.

+

Exogenous regressor models:

+ ++++ + + + + + + + + + + + + + + + + + +

trees

Tree-based models for multivariate time series forecasting.

prophet

Wrapper around Facebook's popular Prophet model for time series forecasting.

sarima

A variant of ARIMA with a user-specified Seasonality.

vector_ar

Vector AutoRegressive model for multivariate time series forecasting.

arima

The classic statistical forecasting model ARIMA (AutoRegressive Integrated Moving Average).

+

Deep Learning models:

+ ++++ + + + + + + + + + + + + + + + + + +

deep_ar

Implementation of Deep AR

autoformer

Implementation of Autoformer.

etsformer

Implementation of ETSformer.

informer

Implementation of Informer.

transformer

Implementation of Transformer for time series data.

+

Note that the AutoML variants +AutoSarima and +AutoProphet +also support exogenous regressors.

+
+

Base classes

+
+

forecast.base

+

Base class for forecasting models.

+
+
+class merlion.models.forecast.base.ForecasterConfig(max_forecast_steps=None, target_seq_index=None, invert_transform=None, transform=None, **kwargs)
+

Bases: Config

+

Config object used to define a forecaster model.

+
+
Parameters
+
    +

  • max_forecast_steps (Optional[int]) – Max # of steps we would like to forecast for. Required for some models like MSES.

    • +

  • target_seq_index (Optional[int]) – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+
+max_forecast_steps: Optional[int] = None
+
+ +
+
+target_seq_index: Optional[int] = None
+
+ +
+
+invert_transform: bool = None
+
+ +
+ +
+
+class merlion.models.forecast.base.ForecasterBase(config)
+

Bases: ModelBase

+

Base class for a forecaster model.

+
+

Note

+

If your model depends on an evenly spaced time series, make sure to

+
    +

  1. Call ForecasterBase.train_pre_process in ForecasterBase.train

    2. +

  2. Call ForecasterBase.resample_time_stamps at the start of +ForecasterBase.forecast to get a set of resampled time stamps, and +call time_series.align(reference=time_stamps) to align the forecast +with the original time stamps.

    3. +
+
+
+
+config_class
+

alias of ForecasterConfig

+
+ +
+
+target_name = None
+

The name of the target univariate to forecast.

+
+ +
+
+property max_forecast_steps
+
+ +
+
+property target_seq_index: int
+
+
Returns
+

the index of the univariate (amongst all univariates in a +general multivariate time series) whose value we would like to forecast.

+
+
+
+ +
+
+property invert_transform
+
+
Returns
+

Whether to automatically invert the transform before returning a forecast.

+
+
+
+ +
+
+property require_univariate: bool
+

All forecasters can work on multivariate data, since they only forecast a single target univariate.

+
+ +
+
+property support_multivariate_output: bool
+

Indicating whether the forecasting model can forecast multivariate output.

+
+ +
+
+resample_time_stamps(time_stamps, time_series_prev=None)
+
+ +
+
+train_pre_process(train_data, exog_data=None, return_exog=None)
+

Applies pre-processing steps common for training most models.

+
+
Parameters
+

train_data (TimeSeries) – the original time series of training data

+
+
Return type
+

Union[TimeSeries, Tuple[TimeSeries, Optional[TimeSeries]]]

+
+
Returns
+

the training data, after any necessary pre-processing has been applied

+
+
+
+ +
+
+train(train_data, train_config=None, exog_data=None)
+

Trains the forecaster on the input time series.

+
+
Parameters
+
    +

  • train_data (TimeSeries) – a TimeSeries of metric values to train the model.

    • +

  • train_config – Additional training configs, if needed. Only required for some models.

    • +

  • exog_data (Optional[TimeSeries]) – A time series of exogenous variables, sampled at the same time stamps as train_data. +Exogenous variables are known a priori, and they are independent of the variable being forecasted. +Only supported for models which inherit from ForecasterExogBase.

    • +
+
+
Return type
+

Tuple[TimeSeries, Optional[TimeSeries]]

+
+
Returns
+

the model’s prediction on train_data, in the same format as +if you called ForecasterBase.forecast on the time stamps of train_data

+
+
+
+ +
+
+train_post_process(train_result)
+

Converts the train result (forecast & stderr for training data) into TimeSeries objects, and inverts the +model’s transform if desired.

+
+
Return type
+

Tuple[TimeSeries, TimeSeries]

+
+
+
+ +
+
+transform_exog_data(exog_data, time_stamps, time_series_prev=None)
+
+
Return type
+

Union[Tuple[TimeSeries, TimeSeries], Tuple[TimeSeries, None], Tuple[None, None]]

+
+
+
+ +
+
+forecast(time_stamps, time_series_prev=None, exog_data=None, return_iqr=False, return_prev=False)
+

Returns the model’s forecast on the timestamps given. If self.transform is specified in the config, the +forecast is a forecast of transformed values by default. To invert the transform and forecast the actual +values of the time series, specify invert_transform = True when specifying the config.

+
+
Parameters
+
    +

  • time_stamps (Union[int, List[int]]) – Either a list of timestamps we wish to forecast for, or the number of steps (int) +we wish to forecast for.

    • +

  • time_series_prev (Optional[TimeSeries]) – a time series immediately preceding time_series. If given, we use it to initialize +the forecaster’s state. Otherwise, we assume that time_series immediately follows the training data.

    • +

  • exog_data (Optional[TimeSeries]) – A time series of exogenous variables. Exogenous variables are known a priori, and they are +independent of the variable being forecasted. exog_data must include data for all of time_stamps; +if time_series_prev is given, it must include data for all of time_series_prev.time_stamps as well. +Optional. Only supported for models which inherit from ForecasterExogBase.

    • +

  • return_iqr (bool) – whether to return the inter-quartile range for the forecast. +Only supported for models which return error bars.

    • +

  • return_prev (bool) – whether to return the forecast for time_series_prev (and its stderr or IQR if relevant), +in addition to the forecast for time_stamps. Only used if time_series_prev is provided.

    • +
+
+
Return type
+

Union[Tuple[TimeSeries, Optional[TimeSeries]], Tuple[TimeSeries, TimeSeries, TimeSeries]]

+
+
Returns
+

(forecast, stderr) if return_iqr is false, (forecast, lb, ub) otherwise.

+
    +

  • forecast: the forecast for the timestamps given

    • +

  • stderr: the standard error of each forecast value. May be None.

    • +

  • lb: 25th percentile of forecast values for each timestamp

    • +

  • ub: 75th percentile of forecast values for each timestamp

    • +
+

+
+
+
+ +
+
+batch_forecast(time_stamps_list, time_series_prev_list, return_iqr=False, return_prev=False)
+

Returns the model’s forecast on a batch of timestamps given.

+
+
Parameters
+
    +

  • time_stamps_list (List[List[int]]) – a list of lists of timestamps we wish to forecast for

    • +

  • time_series_prev_list (List[TimeSeries]) – a list of TimeSeries immediately preceding the time stamps in time_stamps_list

    • +

  • return_iqr (bool) – whether to return the inter-quartile range for the forecast. +Only supported by models which can return error bars.

    • +

  • return_prev (bool) – whether to return the forecast for time_series_prev (and its stderr or IQR if relevant), +in addition to the forecast for time_stamps. Only used if time_series_prev is provided.

    • +
+
+
Return type
+

Tuple[Union[Tuple[List[TimeSeries], List[Optional[TimeSeries]]], Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries]]]]

+
+
Returns
+

(forecast, forecast_stderr) if return_iqr is false, +(forecast, forecast_lb, forecast_ub) otherwise.

+
    +

  • forecast: the forecast for the timestamps given

    • +

  • forecast_stderr: the standard error of each forecast value. May be None.

    • +

  • forecast_lb: 25th percentile of forecast values for each timestamp

    • +

  • forecast_ub: 75th percentile of forecast values for each timestamp

    • +
+

+
+
+
+ +
+
+get_figure(*, time_series=None, time_stamps=None, time_series_prev=None, exog_data=None, plot_forecast_uncertainty=False, plot_time_series_prev=False)
+
+
Parameters
+
    +

  • time_series (Optional[TimeSeries]) – the time series over whose timestamps we wish to make a forecast. Exactly one of +time_series or time_stamps should be provided.

    • +

  • time_stamps (Optional[List[int]]) – Either a list of timestamps we wish to forecast for, or the number of steps (int) +we wish to forecast for. Exactly one of time_series or time_stamps should be provided.

    • +

  • time_series_prev (Optional[TimeSeries]) – a time series immediately preceding time_series. If given, we use it to initialize +the forecaster’s state. Otherwise, we assume that time_series immediately follows the training data.

    • +

  • exog_data (Optional[TimeSeries]) – A time series of exogenous variables. Exogenous variables are known a priori, and they are +independent of the variable being forecasted. exog_data must include data for all of time_stamps; +if time_series_prev is given, it must include data for all of time_series_prev.time_stamps as well. +Optional. Only supported for models which inherit from ForecasterExogBase.

    • +

  • plot_forecast_uncertainty – whether to plot uncertainty estimates (the inter-quartile range) for forecast +values. Not supported for all models.

    • +

  • plot_time_series_prev – whether to plot time_series_prev (and the model’s fit for it). +Only used if time_series_prev is given.

    • +
+
+
Return type
+

Figure

+
+
Returns
+

a Figure of the model’s forecast.

+
+
+
+ +
+
+plot_forecast(*, time_series=None, time_stamps=None, time_series_prev=None, exog_data=None, plot_forecast_uncertainty=False, plot_time_series_prev=False, figsize=(1000, 600), ax=None)
+

Plots the forecast for the time series in matplotlib, optionally also +plotting the uncertainty of the forecast, as well as the past values +(both true and predicted) of the time series.

+
+
Parameters
+
    +

  • time_series (Optional[TimeSeries]) – the time series over whose timestamps we wish to make a forecast. Exactly one of +time_series or time_stamps should be provided.

    • +

  • time_stamps (Optional[List[int]]) – Either a list of timestamps we wish to forecast for, or the number of steps (int) +we wish to forecast for. Exactly one of time_series or time_stamps should be provided.

    • +

  • time_series_prev (Optional[TimeSeries]) – a time series immediately preceding time_series. If given, we use it to initialize +the forecaster’s state. Otherwise, we assume that time_series immediately follows the training data.

    • +

  • exog_data (Optional[TimeSeries]) – A time series of exogenous variables. Exogenous variables are known a priori, and they are +independent of the variable being forecasted. exog_data must include data for all of time_stamps; +if time_series_prev is given, it must include data for all of time_series_prev.time_stamps as well. +Optional. Only supported for models which inherit from ForecasterExogBase.

    • +

  • plot_forecast_uncertainty – whether to plot uncertainty estimates (the inter-quartile range) for forecast +values. Not supported for all models.

    • +

  • plot_time_series_prev – whether to plot time_series_prev (and the model’s fit for it). Only used if +time_series_prev is given.

    • +

  • figsize – figure size in pixels

    • +

  • ax – matplotlib axis to add this plot to

    • +
+
+
Returns
+

(fig, ax): matplotlib figure & axes the figure was plotted on

+
+
+
+ +
+
+plot_forecast_plotly(*, time_series=None, time_stamps=None, time_series_prev=None, exog_data=None, plot_forecast_uncertainty=False, plot_time_series_prev=False, figsize=(1000, 600))
+

Plots the forecast for the time series in plotly, optionally also +plotting the uncertainty of the forecast, as well as the past values +(both true and predicted) of the time series.

+
+
Parameters
+
    +

  • time_series (Optional[TimeSeries]) – the time series over whose timestamps we wish to make a forecast. Exactly one of +time_series or time_stamps should be provided.

    • +

  • time_stamps (Optional[List[int]]) – Either a list of timestamps we wish to forecast for, or the number of steps (int) +we wish to forecast for. Exactly one of time_series or time_stamps should be provided.

    • +

  • time_series_prev (Optional[TimeSeries]) – a time series immediately preceding time_series. If given, we use it to initialize +the forecaster’s state. Otherwise, we assume that time_series immediately follows the training data.

    • +

  • exog_data (Optional[TimeSeries]) – A time series of exogenous variables. Exogenous variables are known a priori, and they are +independent of the variable being forecasted. exog_data must include data for all of time_stamps; +if time_series_prev is given, it must include data for all of time_series_prev.time_stamps as well. +Optional. Only supported for models which inherit from ForecasterExogBase.

    • +

  • plot_forecast_uncertainty – whether to plot uncertainty estimates (the +inter-quartile range) for forecast values. Not supported for all +models.

    • +

  • plot_time_series_prev – whether to plot time_series_prev (and +the model’s fit for it). Only used if time_series_prev is given.

    • +

  • figsize – figure size in pixels

    • +
+
+
+
+ +
+ +
+
+class merlion.models.forecast.base.ForecasterExogConfig(exog_transform=None, exog_aggregation_policy='Mean', exog_missing_value_policy='ZFill', max_forecast_steps=None, target_seq_index=None, invert_transform=None, transform=None, **kwargs)
+

Bases: ForecasterConfig

+
+
Parameters
+
    +

  • exog_transform (Optional[TransformBase]) – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy (Union[AggregationPolicy, str]) – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy (Union[MissingValuePolicy, str]) – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • max_forecast_steps – Max # of steps we would like to forecast for. Required for some models like MSES.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+
+exog_transform: TransformBase = None
+
+ +
+
+property exog_aggregation_policy
+
+ +
+
+property exog_missing_value_policy
+
+ +
+ +
+
+class merlion.models.forecast.base.ForecasterExogBase(config)
+

Bases: ForecasterBase

+

Base class for a forecaster model which supports exogenous variables. Exogenous variables are known a priori, and +they are independent of the variable being forecasted.

+
+
+property supports_exog
+

Whether the model supports exogenous regressors.

+
+ +
+
+property exog_transform
+
+ +
+
+property exog_aggregation_policy
+
+ +
+
+property exog_missing_value_policy
+
+ +
+
+transform_exog_data(exog_data, time_stamps, time_series_prev=None)
+

Transforms & resamples exogenous data and splits it into two subsets: +one with the same timestamps as time_series_prev (None if time_series_prev is None), +and one with the timestamps time_stamps.

+
+
Parameters
+
    +

  • exog_data (TimeSeries) – The exogenous data of interest.

    • +

  • time_stamps (Union[List[int], DatetimeIndex]) – The timestamps of interest (either the timestamps of data, or the timestamps at which +we want to obtain a forecast)

    • +

  • time_series_prev (Optional[TimeSeries]) – The timestamps of a time series preceding time_stamps as context. Optional.

    • +
+
+
Return type
+

Union[Tuple[TimeSeries, TimeSeries], Tuple[TimeSeries, None], Tuple[None, None]]

+
+
Returns
+

(exog_data, exog_data_prev), where exog_data has been resampled to match the time_stamps +and exog_data_prev` has been resampled to match ``time_series_prev.time_stamps.

+
+
+
+ +
+ +
+
+

forecast.deep_base

+

Base class for Deep Learning Forecasting Models

+
+
+class merlion.models.forecast.deep_base.DeepForecasterConfig(n_past, batch_size=32, num_epochs=10, optimizer=Optimizer.Adam, loss_fn=LossFunction.mse, clip_gradient=None, use_gpu=True, ts_encoding='h', lr=0.0001, weight_decay=0.0, valid_fraction=0.2, early_stop_patience=None, transform=None, max_forecast_steps=None, target_seq_index=None, invert_transform=None, **kwargs)
+

Bases: DeepConfig, ForecasterConfig

+

Config object used to define a forecaster with deep model

+
+
Parameters
+
    +

  • n_past (int) – # of past steps used for forecasting future.

    • +

  • batch_size – Batch size of a batch for stochastic training of deep models

    • +

  • num_epochs – Total number of epochs for training.

    • +

  • optimizer – The optimizer for learning the parameters of the deep learning models. The value of optimizer +can be Adam, AdamW, SGD, Adagrad, RMSprop.

    • +

  • loss_fn – Loss function for optimizing deep learning models. The value of loss_fn can be +mse for l2 loss, l1 for l1 loss, huber for huber loss.

    • +

  • clip_gradient – Clipping gradient norm of model parameters before updating. If clip_gradient is None, +then the gradient will not be clipped.

    • +

  • use_gpu – Whether to use gpu for training deep models. If use_gpu = True while thre is no GPU device, +the model will use CPU for training instead.

    • +

  • ts_encoding – whether the timestamp should be encoded to a float vector, which can be used +for training deep learning based time series models; if None, the timestamp is not encoded. +If not None, it represents the frequency for time features encoding options:[s:secondly, t:minutely, h:hourly, +d:daily, b:business days, w:weekly, m:monthly]

    • +

  • lr – Learning rate for optimizing deep learning models.

    • +

  • weight_decay – Weight decay (L2 penalty) (default: 0)

    • +

  • valid_fraction – Fraction of validation set to be split from training data

    • +

  • early_stop_patience – Number of epochs with no improvement after which training will be stopped for +early stopping function. If early_stop_patience = None, the training process will not stop early.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • max_forecast_steps – Max # of steps we would like to forecast for. Required for some models like MSES.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +
+
+
+
+ +
+
+class merlion.models.forecast.deep_base.DeepForecaster(config)
+

Bases: DeepModelBase, ForecasterBase

+

Base class for a deep forecaster model

+
+
+config_class
+

alias of DeepForecasterConfig

+
+ +
+
+property support_multivariate_output: bool
+

Deep models support multivariate output by default.

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+ +
+
+

forecast.sklearn_base

+

Base class for forecasters which use arbitrary sklearn regression models internally.

+
+
+class merlion.models.forecast.sklearn_base.SKLearnForecasterConfig(maxlags=None, max_forecast_steps=None, target_seq_index=None, prediction_stride=1, exog_transform=None, exog_aggregation_policy='Mean', exog_missing_value_policy='ZFill', invert_transform=None, transform=None, **kwargs)
+

Bases: ForecasterExogConfig

+

Configuration class for a SKLearnForecaster.

+
+
Parameters
+
    +

  • maxlags (Optional[int]) – Size of historical window to base the forecast on.

    • +

  • max_forecast_steps (Optional[int]) – Max # of steps we would like to forecast for.

    • +

  • target_seq_index (Optional[int]) – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • prediction_stride (int) –

    the number of steps being forecasted in a single call to underlying the model

    +
      +

    • If univariate: the sequence target of the length of prediction_stride will be utilized, forecasting will +be done autoregressively, with the stride unit of prediction_stride

      • +

    • If multivariate:

      +
      +
        +

      • if = 1: autoregressively forecast all variables in the time series, one step at a time

        • +

      • if > 1: only support directly forecasting the next prediction_stride steps in the future. +Autoregression not supported. Note that the model will set prediction_stride = max_forecast_steps.

        • +
      +
      +
      • +
    +

    • +

  • exog_transform – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+ +
+
+class merlion.models.forecast.sklearn_base.SKLearnForecaster(config)
+

Bases: ForecasterExogBase

+

Wrapper around a sklearn-style model for time series forecasting. The underlying model must support +fit() and predict() methods. The model can be trained to be either an autoregressive model of order +maxlags, or to directly predict the next prediction_stride timestamps from a history of length maxlags.

+

If the data is univariate, the model will predict the next prediction_stride elements of the time series. +It can then use these predictions to autoregressively predict the next prediction_stride elements. If the data +is multivariate, the model will either autoregressively predict the next timestamp of all univariates +(if prediction_stride = 1), or it will directly predict the next prediction_stride timestamps of the target +univariate (if prediction_stride > 1).

+
+
+config_class
+

alias of SKLearnForecasterConfig

+
+ +
+
+model = None
+
+ +
+
+property maxlags: int
+
+ +
+
+property prediction_stride: int
+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

All forecasters can work on multivariate data, since they only forecast a single target univariate.

+
+ +
+ +
+
+
+

Univariate models

+
+

forecast.arima

+

The classic statistical forecasting model ARIMA (AutoRegressive Integrated +Moving Average).

+
+
+class merlion.models.forecast.arima.ArimaConfig(order=(4, 1, 2), seasonal_order=(0, 0, 0, 0), exog_transform: TransformBase = None, exog_aggregation_policy: Union[AggregationPolicy, str] = 'Mean', exog_missing_value_policy: Union[MissingValuePolicy, str] = 'ZFill', max_forecast_steps: int = None, target_seq_index: int = None, invert_transform=None, transform: TransformBase = None, max_score: float = 1000, threshold=None, enable_calibrator=True, enable_threshold=True, **kwargs)
+

Bases: SarimaConfig

+

Configuration class for Arima. Just a Sarima model with seasonal order (0, 0, 0, 0).

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • order – Order is (p, d, q) for an ARIMA(p, d, q) process. d must +be an integer indicating the integration order of the process, while +p and q must be integers indicating the AR and MA orders (so that +all lags up to those orders are included).

    • +

  • seasonal_order – (0, 0, 0, 0) because ARIMA has no seasonal order.

    • +

  • exog_transform – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • max_forecast_steps – Max # of steps we would like to forecast for. Required for some models like MSES.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +
+
+
+
+
+property seasonal_order: Tuple[int, int, int, int]
+
+
Returns
+

(0, 0, 0, 0) because ARIMA has no seasonal order.

+
+
+
+ +
+ +
+
+class merlion.models.forecast.arima.Arima(config)
+

Bases: Sarima

+

Implementation of the classic statistical model ARIMA (AutoRegressive Integrated Moving Average) for forecasting.

+
+
+config_class
+

alias of ArimaConfig

+
+ +
+ +
+
+

forecast.sarima

+

A variant of ARIMA with a user-specified Seasonality.

+
+
+class merlion.models.forecast.sarima.SarimaConfig(order=(4, 1, 2), seasonal_order=(2, 0, 1, 24), exog_transform=None, exog_aggregation_policy='Mean', exog_missing_value_policy='ZFill', max_forecast_steps=None, target_seq_index=None, invert_transform=None, transform=None, max_score=1000, threshold=None, enable_calibrator=True, enable_threshold=True, **kwargs)
+

Bases: ForecasterExogConfig

+

Config class for Sarima (Seasonal AutoRegressive Integrated Moving Average).

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • order (List[int]) – Order is (p, d, q) for an ARIMA(p, d, q) process. d must +be an integer indicating the integration order of the process, while +p and q must be integers indicating the AR and MA orders (so that +all lags up to those orders are included).

    • +

  • seasonal_order (List[int]) – Seasonal order is (P, D, Q, S) for seasonal ARIMA +process, where s is the length of the seasonality cycle (e.g. s=24 +for 24 hours on hourly granularity). P, D, Q are as for ARIMA.

    • +

  • exog_transform – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • max_forecast_steps – Max # of steps we would like to forecast for. Required for some models like MSES.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +
+
+
+
+ +
+
+class merlion.models.forecast.sarima.Sarima(config)
+

Bases: ForecasterExogBase, SeasonalityModel

+

Implementation of the classic statistical model SARIMA (Seasonal +AutoRegressive Integrated Moving Average) for forecasting.

+
+
+config_class
+

alias of SarimaConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property order: Tuple[int, int, int]
+
+
Returns
+

the order (p, d, q) of the model, where p is the AR order, +d is the integration order, and q is the MA order.

+
+
+
+ +
+
+property seasonal_order: Tuple[int, int, int, int]
+
+
Returns
+

the seasonal order (P, D, Q, S) for the seasonal ARIMA +process, where p is the AR order, D is the integration order, +Q is the MA order, and S is the length of the seasonality cycle.

+
+
+
+ +
+
+set_seasonality(theta, train_data)
+

Implement this method to do any model-specific adjustments on the seasonality that was provided by +SeasonalityLayer.

+
+
Parameters
+
    +

  • theta – Seasonality processed by SeasonalityLayer.

    • +

  • train_data (UnivariateTimeSeries) – Training data (or numpy array representing the target univariate) +for any model-specific adjustments you might want to make.

    • +
+
+
+
+ +
+ +
+
+

forecast.ets

+

ETS (Error, Trend, Seasonal) forecasting model.

+
+
+class merlion.models.forecast.ets.ETSConfig(max_forecast_steps=None, target_seq_index=None, error='add', trend='add', damped_trend=True, seasonal='add', seasonal_periods=None, refit=True, invert_transform=None, transform=None, enable_calibrator=False, max_score=1000, threshold=None, enable_threshold=True, **kwargs)
+

Bases: ForecasterConfig

+

Configuration class for ETS model. ETS model is an underlying state space +model consisting of an error term (E), a trend component (T), a seasonal +component (S), and a level component. Each component is flexible with +different traits with additive (‘add’) or multiplicative (‘mul’) formulation. +Refer to https://otexts.com/fpp2/taxonomy.html for more information +about ETS model.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • max_forecast_steps (Optional[int]) – Number of steps we would like to forecast for.

    • +

  • target_seq_index (Optional[int]) – The index of the univariate (amongst all +univariates in a general multivariate time series) whose value we +would like to forecast.

    • +

  • error (str) – The error term. “add” or “mul”.

    • +

  • trend (str) – The trend component. “add”, “mul” or None.

    • +

  • damped_trend (bool) – Whether or not an included trend component is damped.

    • +

  • seasonal (str) – The seasonal component. “add”, “mul” or None.

    • +

  • seasonal_periods (Optional[int]) – The length of the seasonality cycle. None by default.

    • +

  • refit (bool) – if True, refit the full ETS model when time_series_prev is given to the forecast method +(slower). If False, simply perform exponential smoothing (faster).

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • enable_calibrator – False because this config assumes calibrated outputs from the model.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +
+
+
+
+ +
+
+class merlion.models.forecast.ets.ETS(config)
+

Bases: SeasonalityModel, ForecasterBase

+

Implementation of the classic local statistical model ETS (Error, Trend, Seasonal) for forecasting.

+
+
+config_class
+

alias of ETSConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property error
+
+ +
+
+property trend
+
+ +
+
+property damped_trend
+
+ +
+
+property seasonal
+
+ +
+
+property seasonal_periods
+
+ +
+
+set_seasonality(theta, train_data)
+

Implement this method to do any model-specific adjustments on the seasonality that was provided by +SeasonalityLayer.

+
+
Parameters
+
    +

  • theta – Seasonality processed by SeasonalityLayer.

    • +

  • train_data (UnivariateTimeSeries) – Training data (or numpy array representing the target univariate) +for any model-specific adjustments you might want to make.

    • +
+
+
+
+ +
+ +
+
+

forecast.prophet

+

Wrapper around Facebook’s popular Prophet model for time series forecasting.

+
+
+class merlion.models.forecast.prophet.ProphetConfig(max_forecast_steps=None, target_seq_index=None, yearly_seasonality='auto', weekly_seasonality='auto', daily_seasonality='auto', seasonality_mode='additive', holidays=None, uncertainty_samples=100, exog_transform=None, exog_aggregation_policy='Mean', exog_missing_value_policy='ZFill', invert_transform=None, transform=None, max_score=1000, threshold=None, enable_calibrator=True, enable_threshold=True, **kwargs)
+

Bases: ForecasterExogConfig

+

Configuration class for Facebook’s Prophet model, as described by +Taylor & Letham, 2017.

+

Base class of the object used to configure an anomaly detection model.

+
+
Parameters
+
    +

  • max_forecast_steps (Optional[int]) – Max # of steps we would like to forecast for.

    • +

  • target_seq_index (Optional[int]) – The index of the univariate (amongst all +univariates in a general multivariate time series) whose value we +would like to forecast.

    • +

  • yearly_seasonality (Union[bool, int]) – If bool, whether to enable yearly seasonality. +By default, it is activated if there are >= 2 years of history, but +deactivated otherwise. If int, this is the number of Fourier series +components used to model the seasonality (default = 10).

    • +

  • weekly_seasonality (Union[bool, int]) – If bool, whether to enable weekly seasonality. +By default, it is activated if there are >= 2 weeks of history, but +deactivated otherwise. If int, this is the number of Fourier series +components used to model the seasonality (default = 3).

    • +

  • daily_seasonality (Union[bool, int]) – If bool, whether to enable daily seasonality. +By default, it is activated if there are >= 2 days of history, but +deactivated otherwise. If int, this is the number of Fourier series +components used to model the seasonality (default = 4).

    • +

  • seasonality_mode – ‘additive’ (default) or ‘multiplicative’.

    • +

  • holidays – pd.DataFrame with columns holiday (string) and ds (date type) +and optionally columns lower_window and upper_window which specify a +range of days around the date to be included as holidays. +lower_window=-2 will include 2 days prior to the date as holidays. Also +optionally can have a column prior_scale specifying the prior scale for +that holiday. Can also be a dict corresponding to the desired pd.DataFrame.

    • +

  • uncertainty_samples (int) – The number of posterior samples to draw in +order to calibrate the anomaly scores.

    • +

  • exog_transform – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • max_score – maximum possible uncalibrated anomaly score

    • +

  • threshold – the rule to use for thresholding anomaly scores

    • +

  • enable_calibrator – whether to enable a calibrator which +automatically transforms all raw anomaly scores to be z-scores +(i.e. distributed as N(0, 1)).

    • +

  • enable_threshold – whether to enable the thresholding rule +when post-processing anomaly scores

    • +
+
+
+
+ +
+
+class merlion.models.forecast.prophet.Prophet(config)
+

Bases: ForecasterExogBase, SeasonalityModel

+

Facebook’s model for time series forecasting. See docs for ProphetConfig +and Taylor & Letham, 2017 for more details.

+
+
+config_class
+

alias of ProphetConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property yearly_seasonality
+
+ +
+
+property weekly_seasonality
+
+ +
+
+property daily_seasonality
+
+ +
+
+property add_seasonality
+
+ +
+
+property seasonality_mode
+
+ +
+
+property holidays
+
+ +
+
+property uncertainty_samples
+
+ +
+
+set_seasonality(theta, train_data)
+

Implement this method to do any model-specific adjustments on the seasonality that was provided by +SeasonalityLayer.

+
+
Parameters
+
    +

  • theta – Seasonality processed by SeasonalityLayer.

    • +

  • train_data (UnivariateTimeSeries) – Training data (or numpy array representing the target univariate) +for any model-specific adjustments you might want to make.

    • +
+
+
+
+ +
+ +
+
+

forecast.smoother

+

Multi-Scale Exponential Smoother for univariate time series forecasting.

+
+
+class merlion.models.forecast.smoother.MSESConfig(max_forecast_steps, max_backstep=None, recency_weight=0.5, accel_weight=1.0, optimize_acc=True, eta=0.0, rho=0.0, phi=2.0, inflation=1.0, target_seq_index=None, invert_transform=None, transform=None, **kwargs)
+

Bases: ForecasterConfig

+

Configuration class for an MSES forecasting model.

+

Letting w be the recency weight, B the maximum backstep, x_t the last seen data point, +and l_s,t the series of losses for scale s.

+
+\[\begin{split}\begin{align*} +\hat{x}_{t+h} & = \sum_{b=0}^B p_{b} \cdot (x_{t-b} + v_{b+h,t} + a_{b+h,t}) \\ +\space \\ +\text{where} \space\space & v_{b+h,t} = \text{EMA}_w(\Delta_{b+h} x_t) \\ +& a_{b+h,t} = \text{EMA}_w(\Delta_{b+h}^2 x_t) \\ +\text{and} \space\space & p_b = \sigma(z)_b \space\space \\ +\text{if} & \space\space z_b = (b+h)^\phi \cdot \text{EMA}_w(l_{b+h,t}) \cdot \text{RWSE}_w(l_{b+h,t})\\ +\end{align*}\end{split}\]
+
+
Parameters
+
    +

  • max_forecast_steps (int) – Max # of steps we would like to forecast for. Required for some models like MSES.

    • +

  • max_backstep (Optional[int]) – Max backstep to use in forecasting. If we train with x(0),…,x(t), +Then, the b-th model MSES uses will forecast x(t+h) by anchoring at x(t-b) and +predicting xhat(t+h) = x(t-b) + delta_hat(b+h).

    • +

  • recency_weight (float) – The recency weight parameter to use when estimating delta_hat.

    • +

  • accel_weight (float) – The weight to scale the acceleration by when computing delta_hat. +Specifically, delta_hat(b+h) = velocity(b+h) + accel_weight * acceleration(b+h).

    • +

  • optimize_acc (bool) – If True, the acceleration correction will only be used at scales +ranging from 1,…(max_backstep+max_forecast_steps)/2.

    • +

  • eta (float) – The parameter used to control the rate at which recency_weight gets +tuned when online updates are made to the model and losses can be computed.

    • +

  • rho (float) – The parameter that determines what fraction of the overall error is due to +velcity error, while the rest is due to the complement. The error at any scale +will be determined as rho * velocity_error + (1-rho) * loss_error.

    • +

  • phi (float) – The parameter used to exponentially inflate the magnitude of loss error at +different scales. Loss error for scale s will be increased by a factor of phi ** s.

    • +

  • inflation (float) – The inflation exponent to use when computing the distribution +p(b|h) over the models when forecasting at horizon h according to standard +errors of the estimated velocities over the models; inflation=1 is equivalent +to using the softmax function.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+
+property max_scale
+
+ +
+
+property backsteps
+
+ +
+ +
+
+class merlion.models.forecast.smoother.MSESTrainConfig(incremental=True, process_losses=True, tune_recency_weights=False, init_batch_sz=2, train_cadence=None)
+

Bases: object

+

MSES training configuration.

+
+
Parameters
+
    +

  • incremental (bool) – If True, train the MSES model incrementally with the initial +training data at the given train_cadence. This allows MSES to return a +forecast for the training data.

    • +

  • init_batch_sz (int) – The size of the inital training batch for MSES. This is +necessary because MSES cannot predict the past, but needs to start with some +data. This should be very small. 2 is the minimum, and is recommended because +2 will result in the most representative train forecast.

    • +

  • train_cadence (Optional[int]) – The frequency at which the training forecasts will be generated +during incremental training.

    • +
+
+
Param
+

If True, track the losses encountered during incremental initial training.

+
+
Tune_recency_weights
+

If True, tune recency weights during incremental initial +training.

+
+
+
+ +
+
+class merlion.models.forecast.smoother.MSES(config)
+

Bases: ForecasterBase

+

Multi-scale Exponential Smoother (MSES) is a forecasting algorithm modeled heavily +after classical mechanical concepts, namely, velocity and acceleration.

+

Having seen data points of a time series up to time t, MSES forecasts x(t+h) by +anchoring at a value b steps back from the last known value, x(t-b), and estimating the +delta between x(t-b) and x(t+h). The delta over these b+h timesteps, delta(b+h), also known +as the delta at scale b+h, is predicted by estimating the velocity over these timesteps +as well as the change in the velocity, acceleration. Specifically,

+
+

xhat(t+h) = x(t-b) + velocity_hat(b+h) + acceleration_hat(b+h)

+
+

This estimation is done for each b, known as a backstep, from 0, which anchors at x(t), +1,… up to a maximum backstep configurable by the user. The algorithm then takes the +seperate forecasts of x(t+h), indexed by which backstep was used, xhat_b(t+h), and determines +a final forecast: p(b|h) dot xhat_b, where p(b|h) is a distribution over the xhat_b’s that is +determined according to the lowest standard errors of the recency-weighted velocity estimates.

+

Letting w be the recency weight, B the maximum backstep, x_t the last seen data point, +and l_s,t the series of losses for scale s.

+
+
+\[\begin{split}\begin{align*} +\hat{x}_{t+h} & = \sum_{b=0}^B p_{b} \cdot (x_{t-b} + v_{b+h,t} + a_{b+h,t}) \\ +\space \\ +\text{where} \space\space & v_{b+h,t} = \text{EMA}_w(\Delta_{b+h} x_t) \\ +& a_{b+h,t} = \text{EMA}_w(\Delta_{b+h}^2 x_t) \\ +\text{and} \space\space & p_b = \sigma(z)_b \space\space \\ +\text{if} & \space\space z_b = (b+h)^\phi \cdot \text{EMA}_w(l_{b+h,t}) \cdot \text{RWSE}_w(l_{b+h,t})\\ +\end{align*}\end{split}\]
+
+
+
+config_class
+

alias of MSESConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property rho
+
+ +
+
+property backsteps
+
+ +
+
+property max_horizon
+
+ +
+
+update(new_data, tune_recency_weights=True, train_cadence=None)
+

Updates the MSES model with new data that has been acquired since the model’s initial training.

+
+
Parameters
+
    +

  • new_data (DataFrame) – New data that has occured since the last training time.

    • +

  • tune_recency_weights (bool) – If True, the model will first forecast the values at the +new_data’s timestamps, calculate the associated losses, and use these losses +to make updates to the recency weight.

    • +

  • train_cadence – The frequency at which the training forecasts will be generated +during incremental training.

    • +
+
+
Return type
+

Tuple[TimeSeries, TimeSeries]

+
+
+
+ +
+
+xhat_h(horizon)
+

Returns the forecasts for the input horizon at every backstep.

+
+
Return type
+

List[Optional[float]]

+
+
+
+ +
+
+marginalize_xhat_h(horizon, xhat_h)
+

Given a list of forecasted values produced by delta estimators at +different backsteps, compute a weighted average of these values. The +weights are assigned based on the standard errors of the velocities, +where the b’th estimate will be given more weight if its velocity has a +lower standard error relative to the other estimates.

+
+
Parameters
+
    +

  • horizon (int) – the horizon at which we want to predict

    • +

  • xhat_h (List[Optional[float]]) – the forecasted values at this horizon, using each of +the possible backsteps

    • +
+
+
+
+ +
+ +
+
+class merlion.models.forecast.smoother.DeltaStats(scale, recency_weight)
+

Bases: object

+

A wrapper around the statistics used to estimate deltas at a given scale.

+
+
Parameters
+
    +

  • scale (int) – The scale associated with the statistics

    • +

  • recency_weight (float) – The recency weight parameter that that the incremental +velocity, acceleration and standard error statistics should use.

    • +
+
+
+
+
+property lag
+
+ +
+
+update_velocity(vels)
+
+ +
+
+update_acceleration(accs)
+
+ +
+
+update_loss(losses)
+
+ +
+
+tune(losses, eta)
+

Tunes the recency weight according to recent forecast losses.

+
+
Parameters
+
    +

  • losses (List[float]) – List of recent losses.

    • +

  • eta (float) – Constant by which to scale the update to the recency weight. +A bigger eta means more aggressive updates to the recency_weight.

    • +
+
+
+
+ +
+ +
+
+class merlion.models.forecast.smoother.DeltaEstimator(max_scale, recency_weight, accel_weight, optimize_acc, eta, phi, data=None, stats=None)
+

Bases: object

+

Class for estimating the delta for MSES.

+
+
Parameters
+
    +

  • max_scale (int) – Delta Estimator can estimate delta over multiple scales, or +time steps, ranging from 1,2,…,max_scale.

    • +

  • recency_weight (float) – The recency weight parameter to use when estimating delta_hat.

    • +

  • accel_weight (float) – The weight to scale the acceleration by when computing delta_hat. +Specifically, delta_hat(b+h) = velocity(b+h) + accel_weight * acceleration(b+h).

    • +

  • optimize_acc (bool) – If True, the acceleration correction will only be used at scales +ranging from 1,…,max_scale/2.

    • +

  • eta (float) – The parameter used to control the rate at which recency_weight gets +tuned when online updates are made to the model and losses can be computed.

    • +

  • data (Optional[UnivariateTimeSeries]) – The data to initialize the delta estimator with.

    • +

  • stats (Optional[Dict[int, DeltaStats]]) – Dictionary mapping scales to DeltaStats objects to be used for delta +estimation.

    • +
+
+
+
+
+property acc_max_scale
+
+ +
+
+property max_scale
+
+ +
+
+property data
+
+ +
+
+property x
+
+ +
+
+train(new_data)
+

Updates the delta statistics: velocity, acceleration and velocity +standard error at each scale using new data.

+
+
Parameters
+

new_data (UnivariateTimeSeries) – new datapoints in the time series.

+
+
+
+ +
+
+process_losses(scale_losses, tune_recency_weights=False)
+

Uses recent forecast errors to improve the delta estimator. This is done by updating +the recency_weight that is used by delta stats at particular scales.

+
+
Parameters
+

scale_losses (Dict[int, List[float]]) – A dictionary mapping a scale to a list of forecasting errors +that associated with that scale.

+
+
+
+ +
+
+velocity(scale)
+
+
Return type
+

float

+
+
+
+ +
+
+acceleration(scale)
+
+
Return type
+

float

+
+
+
+ +
+
+vel_err(scale)
+
+
Return type
+

float

+
+
+
+ +
+
+pos_err(scale)
+
+
Return type
+

float

+
+
+
+ +
+
+neg_err(scale)
+
+
Return type
+

float

+
+
+
+ +
+
+loss_err(scale)
+
+
Return type
+

float

+
+
+
+ +
+
+delta_hat(scale)
+
+
Return type
+

float

+
+
+
+ +
+ +
+
+
+

Multivariate models

+
+

forecast.vector_ar

+

Vector AutoRegressive model for multivariate time series forecasting.

+
+
+class merlion.models.forecast.vector_ar.VectorARConfig(maxlags=None, target_seq_index=None, exog_transform=None, exog_aggregation_policy='Mean', exog_missing_value_policy='ZFill', max_forecast_steps=None, invert_transform=None, transform=None, **kwargs)
+

Bases: ForecasterExogConfig

+

Config object for VectorAR forecaster.

+
+
Parameters
+
    +

  • maxlags (Optional[int]) – Max # of lags for AR

    • +

  • target_seq_index (Optional[int]) – The index of the univariate (amongst all +univariates in a general multivariate time series) whose value we +would like to forecast.

    • +

  • exog_transform – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • max_forecast_steps – Max # of steps we would like to forecast for. Required for some models like MSES.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+ +
+
+class merlion.models.forecast.vector_ar.VectorAR(config)
+

Bases: ForecasterExogBase

+

Vector AutoRegressive model for multivariate time series forecasting.

+
+
+config_class
+

alias of VectorARConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property maxlags: int
+
+ +
+ +
+
+

forecast.trees

+

Tree-based models for multivariate time series forecasting.

+
+
+class merlion.models.forecast.trees.RandomForestForecasterConfig(min_samples_split=2, n_estimators=100, max_depth=None, random_state=None, maxlags=None, max_forecast_steps=None, target_seq_index=None, prediction_stride=1, exog_transform=None, exog_aggregation_policy='Mean', exog_missing_value_policy='ZFill', invert_transform=None, transform=None, **kwargs)
+

Bases: _TreeEnsembleForecasterConfig

+

Config class for RandomForestForecaster.

+
+
Parameters
+
    +

  • min_samples_split (int) – min split for tree leaves

    • +

  • n_estimators – number of base estimators for the tree ensemble

    • +

  • max_depth – max depth of base estimators

    • +

  • random_state – random seed for bagging

    • +

  • maxlags – Size of historical window to base the forecast on.

    • +

  • max_forecast_steps – Max # of steps we would like to forecast for.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • prediction_stride –

    the number of steps being forecasted in a single call to underlying the model

    +
      +

    • If univariate: the sequence target of the length of prediction_stride will be utilized, forecasting will +be done autoregressively, with the stride unit of prediction_stride

      • +

    • If multivariate:

      +
      +
        +

      • if = 1: autoregressively forecast all variables in the time series, one step at a time

        • +

      • if > 1: only support directly forecasting the next prediction_stride steps in the future. +Autoregression not supported. Note that the model will set prediction_stride = max_forecast_steps.

        • +
      +
      +
      • +
    +

    • +

  • exog_transform – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+ +
+
+class merlion.models.forecast.trees.RandomForestForecaster(config)
+

Bases: SKLearnForecaster

+

Random Forest Regressor for time series forecasting

+

Random Forest is a meta estimator that fits a number of classifying decision +trees on various sub-samples of the dataset, and uses averaging to improve +the predictive accuracy and control over-fitting.

+
+
+config_class
+

alias of RandomForestForecasterConfig

+
+ +
+ +
+
+class merlion.models.forecast.trees.ExtraTreesForecasterConfig(min_samples_split=2, n_estimators=100, max_depth=None, random_state=None, maxlags=None, max_forecast_steps=None, target_seq_index=None, prediction_stride=1, exog_transform=None, exog_aggregation_policy='Mean', exog_missing_value_policy='ZFill', invert_transform=None, transform=None, **kwargs)
+

Bases: _TreeEnsembleForecasterConfig

+

Config class for ExtraTreesForecaster.

+
+
Parameters
+
    +

  • min_samples_split (int) – min split for tree leaves

    • +

  • n_estimators – number of base estimators for the tree ensemble

    • +

  • max_depth – max depth of base estimators

    • +

  • random_state – random seed for bagging

    • +

  • maxlags – Size of historical window to base the forecast on.

    • +

  • max_forecast_steps – Max # of steps we would like to forecast for.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • prediction_stride –

    the number of steps being forecasted in a single call to underlying the model

    +
      +

    • If univariate: the sequence target of the length of prediction_stride will be utilized, forecasting will +be done autoregressively, with the stride unit of prediction_stride

      • +

    • If multivariate:

      +
      +
        +

      • if = 1: autoregressively forecast all variables in the time series, one step at a time

        • +

      • if > 1: only support directly forecasting the next prediction_stride steps in the future. +Autoregression not supported. Note that the model will set prediction_stride = max_forecast_steps.

        • +
      +
      +
      • +
    +

    • +

  • exog_transform – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+ +
+
+class merlion.models.forecast.trees.ExtraTreesForecaster(config)
+

Bases: SKLearnForecaster

+

Extra Trees Regressor for time series forecasting

+

Extra Trees Regressor implements a meta estimator that fits a number of +randomized decision trees (a.k.a. extra-trees) on various sub-samples of +the dataset and uses averaging to improve the predictive accuracy and +control over-fitting.

+
+
+config_class
+

alias of ExtraTreesForecasterConfig

+
+ +
+ +
+
+class merlion.models.forecast.trees.LGBMForecasterConfig(learning_rate=0.1, n_jobs=-1, n_estimators=100, max_depth=None, random_state=None, maxlags=None, max_forecast_steps=None, target_seq_index=None, prediction_stride=1, exog_transform=None, exog_aggregation_policy='Mean', exog_missing_value_policy='ZFill', invert_transform=None, transform=None, **kwargs)
+

Bases: _TreeEnsembleForecasterConfig

+

Config class for LGBMForecaster.

+
+
Parameters
+
    +

  • learning_rate (float) – learning rate for boosting

    • +

  • n_jobs (int) – num of threading, -1 or 0 indicates device default, positive int indicates num of threads

    • +

  • n_estimators – number of base estimators for the tree ensemble

    • +

  • max_depth – max depth of base estimators

    • +

  • random_state – random seed for bagging

    • +

  • maxlags – Size of historical window to base the forecast on.

    • +

  • max_forecast_steps – Max # of steps we would like to forecast for.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • prediction_stride –

    the number of steps being forecasted in a single call to underlying the model

    +
      +

    • If univariate: the sequence target of the length of prediction_stride will be utilized, forecasting will +be done autoregressively, with the stride unit of prediction_stride

      • +

    • If multivariate:

      +
      +
        +

      • if = 1: autoregressively forecast all variables in the time series, one step at a time

        • +

      • if > 1: only support directly forecasting the next prediction_stride steps in the future. +Autoregression not supported. Note that the model will set prediction_stride = max_forecast_steps.

        • +
      +
      +
      • +
    +

    • +

  • exog_transform – The pre-processing transform for exogenous data. Note: resampling is handled separately.

    • +

  • exog_aggregation_policy – The policy to use for aggregating values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • exog_missing_value_policy – The policy to use for imputing missing values in exogenous data, +to ensure it is sampled at the same timestamps as the endogenous data.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+ +
+
+class merlion.models.forecast.trees.LGBMForecaster(config)
+

Bases: SKLearnForecaster

+

Light gradient boosting (LGBM) regressor for time series forecasting

+

LightGBM is a light weight and fast gradient boosting framework that uses tree based learning algorithms, for more +details, please refer to the document https://lightgbm.readthedocs.io/en/latest/Features.html

+
+
+config_class
+

alias of LGBMForecasterConfig

+
+ +
+ +
+
+

forecast.deep_ar

+

Implementation of Deep AR

+
+
+class merlion.models.forecast.deep_ar.DeepARConfig(n_past, max_forecast_steps=None, hidden_size=32, num_hidden_layers=2, lags_seq=[1], num_prediction_samples=10, loss_fn=LossFunction.guassian_nll, **kwargs)
+

Bases: DeepForecasterConfig, NormalizingConfig

+

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks: https://arxiv.org/abs/1704.04110

+
+
Parameters
+
    +

  • n_past – # of past steps used for forecasting future.

    • +

  • max_forecast_steps (Optional[int]) – Max # of steps we would like to forecast for.

    • +

  • hidden_size (Optional[int]) – hidden_size of the LSTM layers

    • +

  • num_hidden_layers (int) – # of hidden layers in LSTM

    • +

  • lags_seq (List[int]) – Indices of the lagged observations that the RNN takes as input. For example, +[1] indicates that the RNN only takes the observation at time t-1 to produce the +output for time t.

    • +

  • num_prediction_samples (int) – # of samples to produce the forecasting

    • +

  • loss_fn (Union[str, LossFunction]) – Loss function for optimizing deep learning models. The value of loss_fn can be +mse for l2 loss, l1 for l1 loss, huber for huber loss.

    • +

  • batch_size – Batch size of a batch for stochastic training of deep models

    • +

  • num_epochs – Total number of epochs for training.

    • +

  • optimizer – The optimizer for learning the parameters of the deep learning models. The value of optimizer +can be Adam, AdamW, SGD, Adagrad, RMSprop.

    • +

  • clip_gradient – Clipping gradient norm of model parameters before updating. If clip_gradient is None, +then the gradient will not be clipped.

    • +

  • use_gpu – Whether to use gpu for training deep models. If use_gpu = True while thre is no GPU device, +the model will use CPU for training instead.

    • +

  • ts_encoding – whether the timestamp should be encoded to a float vector, which can be used +for training deep learning based time series models; if None, the timestamp is not encoded. +If not None, it represents the frequency for time features encoding options:[s:secondly, t:minutely, h:hourly, +d:daily, b:business days, w:weekly, m:monthly]

    • +

  • lr – Learning rate for optimizing deep learning models.

    • +

  • weight_decay – Weight decay (L2 penalty) (default: 0)

    • +

  • valid_fraction – Fraction of validation set to be split from training data

    • +

  • early_stop_patience – Number of epochs with no improvement after which training will be stopped for +early stopping function. If early_stop_patience = None, the training process will not stop early.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • normalize – Pre-trained normalization transformation (optional).

    • +
+
+
+
+ +
+
+class merlion.models.forecast.deep_ar.DeepARModel(config)
+

Bases: TorchModel

+

Implementaion of Deep AR model

+

Initializes internal Module state, shared by both nn.Module and ScriptModule.

+
+
+static get_lagged_subsequences(sequence, sequence_length, indices, subsequences_length=1)
+
+
Return type
+

Tensor

+
+
+
+ +
+
+unroll_encoder(past, past_timestamp, future_timestamp, future=None)
+
+ +
+
+calculate_loss(past, past_timestamp, future, future_timestamp)
+
+ +
+
+sampling_decoder(past, time_features, begin_states)
+
+ +
+
+forward(past, past_timestamp, future_timestamp, mean_samples=True)
+

Defines the computation performed at every call.

+

Should be overridden by all subclasses.

+
+

Note

+

Although the recipe for forward pass needs to be defined within +this function, one should call the Module instance afterwards +instead of this since the former takes care of running the +registered hooks while the latter silently ignores them.

+
+
+ +
+ +
+
+class merlion.models.forecast.deep_ar.DeepARForecaster(config)
+

Bases: DeepForecaster

+

Implementaion of Deep AR model forecaster

+
+
+config_class
+

alias of DeepARConfig

+
+ +
+
+deep_model_class
+

alias of DeepARModel

+
+ +
+ +
+
+

forecast.autoformer

+

Implementation of Autoformer.

+
+
+class merlion.models.forecast.autoformer.AutoformerConfig(n_past, max_forecast_steps=None, moving_avg=25, encoder_input_size=None, decoder_input_size=None, num_encoder_layers=2, num_decoder_layers=1, start_token_len=0, factor=3, model_dim=512, embed='timeF', dropout=0.05, activation='gelu', n_heads=8, fcn_dim=2048, **kwargs)
+

Bases: DeepForecasterConfig, NormalizingConfig

+

Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting: https://arxiv.org/abs/2106.13008. +Code adapted from https://github.com/thuml/Autoformer.

+
+
Parameters
+
    +

  • n_past – # of past steps used for forecasting future.

    • +

  • max_forecast_steps (Optional[int]) – Max # of steps we would like to forecast for.

    • +

  • moving_avg (int) – Window size of moving average for Autoformer.

    • +

  • encoder_input_size (Optional[int]) – Input size of encoder. If encoder_input_size = None, +then the model will automatically use config.dim, which is the dimension of the input data.

    • +

  • decoder_input_size (Optional[int]) – Input size of decoder. If decoder_input_size = None, +then the model will automatically use config.dim, which is the dimension of the input data.

    • +

  • num_encoder_layers (int) – Number of encoder layers.

    • +

  • num_decoder_layers (int) – Number of decoder layers.

    • +

  • start_token_len (int) – Length of start token for deep transformer encoder-decoder based models. +The start token is similar to the special tokens for NLP models (e.g., bos, sep, eos tokens).

    • +

  • factor (int) – Attention factor.

    • +

  • model_dim (int) – Dimension of the model.

    • +

  • embed (str) – Time feature encoding type, options include timeF, fixed and learned.

    • +

  • dropout (float) – dropout rate.

    • +

  • activation (str) – Activation function, can be gelu, relu, sigmoid, etc.

    • +

  • n_heads (int) – Number of heads of the model.

    • +

  • fcn_dim (int) – Hidden dimension of the MLP layer in the model.

    • +

  • batch_size – Batch size of a batch for stochastic training of deep models

    • +

  • num_epochs – Total number of epochs for training.

    • +

  • optimizer – The optimizer for learning the parameters of the deep learning models. The value of optimizer +can be Adam, AdamW, SGD, Adagrad, RMSprop.

    • +

  • loss_fn – Loss function for optimizing deep learning models. The value of loss_fn can be +mse for l2 loss, l1 for l1 loss, huber for huber loss.

    • +

  • clip_gradient – Clipping gradient norm of model parameters before updating. If clip_gradient is None, +then the gradient will not be clipped.

    • +

  • use_gpu – Whether to use gpu for training deep models. If use_gpu = True while thre is no GPU device, +the model will use CPU for training instead.

    • +

  • ts_encoding – whether the timestamp should be encoded to a float vector, which can be used +for training deep learning based time series models; if None, the timestamp is not encoded. +If not None, it represents the frequency for time features encoding options:[s:secondly, t:minutely, h:hourly, +d:daily, b:business days, w:weekly, m:monthly]

    • +

  • lr – Learning rate for optimizing deep learning models.

    • +

  • weight_decay – Weight decay (L2 penalty) (default: 0)

    • +

  • valid_fraction – Fraction of validation set to be split from training data

    • +

  • early_stop_patience – Number of epochs with no improvement after which training will be stopped for +early stopping function. If early_stop_patience = None, the training process will not stop early.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • normalize – Pre-trained normalization transformation (optional).

    • +
+
+
+
+ +
+
+class merlion.models.forecast.autoformer.AutoformerModel(config)
+

Bases: TorchModel

+

Implementaion of Autoformer deep torch model.

+

Initializes internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(past, past_timestamp, future_timestamp, enc_self_mask=None, dec_self_mask=None, dec_enc_mask=None, **kwargs)
+

Defines the computation performed at every call.

+

Should be overridden by all subclasses.

+
+

Note

+

Although the recipe for forward pass needs to be defined within +this function, one should call the Module instance afterwards +instead of this since the former takes care of running the +registered hooks while the latter silently ignores them.

+
+
+ +
+ +
+
+class merlion.models.forecast.autoformer.AutoformerForecaster(config)
+

Bases: DeepForecaster

+

Implementaion of Autoformer deep forecaster.

+
+
+config_class
+

alias of AutoformerConfig

+
+ +
+
+deep_model_class
+

alias of AutoformerModel

+
+ +
+ +
+
+

forecast.etsformer

+

Implementation of ETSformer.

+
+
+class merlion.models.forecast.etsformer.ETSformerConfig(n_past, max_forecast_steps=None, encoder_input_size=None, decoder_input_size=None, num_encoder_layers=2, num_decoder_layers=2, model_dim=512, dropout=0.2, n_heads=8, fcn_dim=2048, top_K=1, sigma=0.2, **kwargs)
+

Bases: DeepForecasterConfig, NormalizingConfig

+

ETSformer: Exponential Smoothing Transformers for Time-series Forecasting: https://arxiv.org/abs/2202.01381 +Code adapted from https://github.com/salesforce/ETSformer.

+
+
Parameters
+
    +

  • n_past – # of past steps used for forecasting future.

    • +

  • max_forecast_steps (Optional[int]) – Max # of steps we would like to forecast for.

    • +

  • encoder_input_size (Optional[int]) – Input size of encoder. If encoder_input_size = None, +then the model will automatically use config.dim, which is the dimension of the input data.

    • +

  • decoder_input_size (Optional[int]) – Input size of decoder. If decoder_input_size = None, +then the model will automatically use config.dim, which is the dimension of the input data.

    • +

  • num_encoder_layers (int) – Number of encoder layers.

    • +

  • num_decoder_layers (int) – Number of decoder layers.

    • +

  • model_dim (int) – Dimension of the model.

    • +

  • dropout (float) – dropout rate.

    • +

  • n_heads (int) – Number of heads of the model.

    • +

  • fcn_dim (int) – Hidden dimension of the MLP layer in the model.

    • +

  • top_K (int) – Top-K Frequent Fourier basis.

    • +

  • sigma – Standard derivation for ETS input data transform.

    • +

  • batch_size – Batch size of a batch for stochastic training of deep models

    • +

  • num_epochs – Total number of epochs for training.

    • +

  • optimizer – The optimizer for learning the parameters of the deep learning models. The value of optimizer +can be Adam, AdamW, SGD, Adagrad, RMSprop.

    • +

  • loss_fn – Loss function for optimizing deep learning models. The value of loss_fn can be +mse for l2 loss, l1 for l1 loss, huber for huber loss.

    • +

  • clip_gradient – Clipping gradient norm of model parameters before updating. If clip_gradient is None, +then the gradient will not be clipped.

    • +

  • use_gpu – Whether to use gpu for training deep models. If use_gpu = True while thre is no GPU device, +the model will use CPU for training instead.

    • +

  • ts_encoding – whether the timestamp should be encoded to a float vector, which can be used +for training deep learning based time series models; if None, the timestamp is not encoded. +If not None, it represents the frequency for time features encoding options:[s:secondly, t:minutely, h:hourly, +d:daily, b:business days, w:weekly, m:monthly]

    • +

  • lr – Learning rate for optimizing deep learning models.

    • +

  • weight_decay – Weight decay (L2 penalty) (default: 0)

    • +

  • valid_fraction – Fraction of validation set to be split from training data

    • +

  • early_stop_patience – Number of epochs with no improvement after which training will be stopped for +early stopping function. If early_stop_patience = None, the training process will not stop early.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • normalize – Pre-trained normalization transformation (optional).

    • +
+
+
+
+ +
+
+class merlion.models.forecast.etsformer.ETSformerModel(config)
+

Bases: TorchModel

+

Implementaion of ETSformer deep torch model.

+

Initializes internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(past, past_timestamp, future_timestamp, enc_self_mask=None, dec_self_mask=None, dec_enc_mask=None, attention=False, **kwargs)
+

Defines the computation performed at every call.

+

Should be overridden by all subclasses.

+
+

Note

+

Although the recipe for forward pass needs to be defined within +this function, one should call the Module instance afterwards +instead of this since the former takes care of running the +registered hooks while the latter silently ignores them.

+
+
+ +
+
+transform(x)
+
+ +
+
+jitter(x)
+
+ +
+
+scale(x)
+
+ +
+
+shift(x)
+
+ +
+ +
+
+class merlion.models.forecast.etsformer.ETSformerForecaster(config)
+

Bases: DeepForecaster

+

Implementaion of ETSformer deep forecaster.

+
+
+config_class
+

alias of ETSformerConfig

+
+ +
+
+deep_model_class
+

alias of ETSformerModel

+
+ +
+ +
+
+

forecast.informer

+

Implementation of Informer.

+
+
+class merlion.models.forecast.informer.InformerConfig(n_past, max_forecast_steps=None, encoder_input_size=None, decoder_input_size=None, num_encoder_layers=2, num_decoder_layers=1, start_token_len=0, factor=3, model_dim=512, embed='timeF', dropout=0.05, activation='gelu', n_heads=8, fcn_dim=2048, distil=True, **kwargs)
+

Bases: DeepForecasterConfig, NormalizingConfig

+

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting: https://arxiv.org/abs/2012.07436 +Code adapted from https://github.com/thuml/Autoformer.

+
+
Parameters
+
    +

  • n_past – # of past steps used for forecasting future.

    • +

  • max_forecast_steps (Optional[int]) – Max # of steps we would like to forecast for.

    • +

  • encoder_input_size (Optional[int]) – Input size of encoder. If encoder_input_size = None, +then the model will automatically use config.dim, which is the dimension of the input data.

    • +

  • decoder_input_size (Optional[int]) – Input size of decoder. If decoder_input_size = None, +then the model will automatically use config.dim, which is the dimension of the input data.

    • +

  • num_encoder_layers (int) – Number of encoder layers.

    • +

  • num_decoder_layers (int) – Number of decoder layers.

    • +

  • start_token_len (int) – Length of start token for deep transformer encoder-decoder based models. +The start token is similar to the special tokens for NLP models (e.g., bos, sep, eos tokens).

    • +

  • factor (int) – Attention factor.

    • +

  • model_dim (int) – Dimension of the model.

    • +

  • embed (str) – Time feature encoding type, options include timeF, fixed and learned.

    • +

  • dropout (float) – dropout rate.

    • +

  • activation (str) – Activation function, can be gelu, relu, sigmoid, etc.

    • +

  • n_heads (int) – Number of heads of the model.

    • +

  • fcn_dim (int) – Hidden dimension of the MLP layer in the model.

    • +

  • distil (bool) – whether to use distilling in the encoder of the model.

    • +

  • batch_size – Batch size of a batch for stochastic training of deep models

    • +

  • num_epochs – Total number of epochs for training.

    • +

  • optimizer – The optimizer for learning the parameters of the deep learning models. The value of optimizer +can be Adam, AdamW, SGD, Adagrad, RMSprop.

    • +

  • loss_fn – Loss function for optimizing deep learning models. The value of loss_fn can be +mse for l2 loss, l1 for l1 loss, huber for huber loss.

    • +

  • clip_gradient – Clipping gradient norm of model parameters before updating. If clip_gradient is None, +then the gradient will not be clipped.

    • +

  • use_gpu – Whether to use gpu for training deep models. If use_gpu = True while thre is no GPU device, +the model will use CPU for training instead.

    • +

  • ts_encoding – whether the timestamp should be encoded to a float vector, which can be used +for training deep learning based time series models; if None, the timestamp is not encoded. +If not None, it represents the frequency for time features encoding options:[s:secondly, t:minutely, h:hourly, +d:daily, b:business days, w:weekly, m:monthly]

    • +

  • lr – Learning rate for optimizing deep learning models.

    • +

  • weight_decay – Weight decay (L2 penalty) (default: 0)

    • +

  • valid_fraction – Fraction of validation set to be split from training data

    • +

  • early_stop_patience – Number of epochs with no improvement after which training will be stopped for +early stopping function. If early_stop_patience = None, the training process will not stop early.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • normalize – Pre-trained normalization transformation (optional).

    • +
+
+
+
+ +
+
+class merlion.models.forecast.informer.InformerModel(config)
+

Bases: TorchModel

+

Implementaion of informer deep torch model.

+

Initializes internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(past, past_timestamp, future_timestamp, enc_self_mask=None, dec_self_mask=None, dec_enc_mask=None, **kwargs)
+

Defines the computation performed at every call.

+

Should be overridden by all subclasses.

+
+

Note

+

Although the recipe for forward pass needs to be defined within +this function, one should call the Module instance afterwards +instead of this since the former takes care of running the +registered hooks while the latter silently ignores them.

+
+
+ +
+ +
+
+class merlion.models.forecast.informer.InformerForecaster(config)
+

Bases: DeepForecaster

+

Implementaion of Informer deep forecaster.

+
+
+config_class
+

alias of InformerConfig

+
+ +
+
+deep_model_class
+

alias of InformerModel

+
+ +
+ +
+
+

forecast.transformer

+

Implementation of Transformer for time series data.

+
+
+class merlion.models.forecast.transformer.TransformerConfig(n_past, max_forecast_steps=None, encoder_input_size=None, decoder_input_size=None, num_encoder_layers=2, num_decoder_layers=1, start_token_len=0, factor=3, model_dim=512, embed='timeF', dropout=0.05, activation='gelu', n_heads=8, fcn_dim=2048, distil=True, **kwargs)
+

Bases: DeepForecasterConfig, NormalizingConfig

+

Transformer for time series forecasting. +Code adapted from https://github.com/thuml/Autoformer.

+
+
Parameters
+
    +

  • n_past – # of past steps used for forecasting future.

    • +

  • max_forecast_steps (Optional[int]) – Max # of steps we would like to forecast for.

    • +

  • encoder_input_size (Optional[int]) – Input size of encoder. If encoder_input_size = None, +then the model will automatically use config.dim, which is the dimension of the input data.

    • +

  • decoder_input_size (Optional[int]) – Input size of decoder. If decoder_input_size = None, +then the model will automatically use config.dim, which is the dimension of the input data.

    • +

  • num_encoder_layers (int) – Number of encoder layers.

    • +

  • num_decoder_layers (int) – Number of decoder layers.

    • +

  • start_token_len (int) – Length of start token for deep transformer encoder-decoder based models. +The start token is similar to the special tokens for NLP models (e.g., bos, sep, eos tokens).

    • +

  • factor (int) – Attention factor.

    • +

  • model_dim (int) – Dimension of the model.

    • +

  • embed (str) – Time feature encoding type, options include timeF, fixed and learned.

    • +

  • dropout (float) – dropout rate.

    • +

  • activation (str) – Activation function, can be gelu, relu, sigmoid, etc.

    • +

  • n_heads (int) – Number of heads of the model.

    • +

  • fcn_dim (int) – Hidden dimension of the MLP layer in the model.

    • +

  • distil (bool) – whether to use distilling in the encoder of the model.

    • +

  • batch_size – Batch size of a batch for stochastic training of deep models

    • +

  • num_epochs – Total number of epochs for training.

    • +

  • optimizer – The optimizer for learning the parameters of the deep learning models. The value of optimizer +can be Adam, AdamW, SGD, Adagrad, RMSprop.

    • +

  • loss_fn – Loss function for optimizing deep learning models. The value of loss_fn can be +mse for l2 loss, l1 for l1 loss, huber for huber loss.

    • +

  • clip_gradient – Clipping gradient norm of model parameters before updating. If clip_gradient is None, +then the gradient will not be clipped.

    • +

  • use_gpu – Whether to use gpu for training deep models. If use_gpu = True while thre is no GPU device, +the model will use CPU for training instead.

    • +

  • ts_encoding – whether the timestamp should be encoded to a float vector, which can be used +for training deep learning based time series models; if None, the timestamp is not encoded. +If not None, it represents the frequency for time features encoding options:[s:secondly, t:minutely, h:hourly, +d:daily, b:business days, w:weekly, m:monthly]

    • +

  • lr – Learning rate for optimizing deep learning models.

    • +

  • weight_decay – Weight decay (L2 penalty) (default: 0)

    • +

  • valid_fraction – Fraction of validation set to be split from training data

    • +

  • early_stop_patience – Number of epochs with no improvement after which training will be stopped for +early stopping function. If early_stop_patience = None, the training process will not stop early.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • target_seq_index – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • invert_transform – Whether to automatically invert the transform before returning a forecast. +By default, we will invert the transform for all base forecasters if it supports a proper inversion, but +we will not invert it for forecaster-based anomaly detectors or transforms without proper inversions.

    • +

  • normalize – Pre-trained normalization transformation (optional).

    • +
+
+
+
+ +
+
+class merlion.models.forecast.transformer.TransformerModel(config)
+

Bases: TorchModel

+

Implementaion of Transformer deep torch model.

+

Initializes internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(past, past_timestamp, future_timestamp, enc_self_mask=None, dec_self_mask=None, dec_enc_mask=None, **kwargs)
+

Defines the computation performed at every call.

+

Should be overridden by all subclasses.

+
+

Note

+

Although the recipe for forward pass needs to be defined within +this function, one should call the Module instance afterwards +instead of this since the former takes care of running the +registered hooks while the latter silently ignores them.

+
+
+ +
+ +
+
+class merlion.models.forecast.transformer.TransformerForecaster(config)
+

Bases: DeepForecaster

+

Implementaion of Transformer deep forecaster

+
+
+config_class
+

alias of TransformerConfig

+
+ +
+
+deep_model_class
+

alias of TransformerModel

+
+ +
+ +
+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.models.html b/v2.0.2/merlion.models.html new file mode 100644 index 000000000..8850849ed --- /dev/null +++ b/v2.0.2/merlion.models.html @@ -0,0 +1,1589 @@ + + + + + + merlion.models package — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

merlion.models package

+

Broadly, Merlion contains two types of models: anomaly detection (merlion.models.anomaly) +and forecasting (merlion.models.forecast). Note that there is a distinct subset of anomaly +detection models that use forecasting models at their core (merlion.models.anomaly.forecast_based).

+

We implement an abstract ModelBase class which provides the following functionality for all models:

+
    +

  1. model = ModelClass(config)

    +
      +

    • initialization with a model-specific config (which inherits from Config)

      • +

    • configs contain:

      +
        +

      • a (potentially trainable) data pre-processing transform from merlion.transform; +note that model.transform is a property which refers to model.config.transform

        • +

      • model-specific hyperparameters

        • +
      +
      • +
    +
    2. +

  2. model.save(dirname, save_config=None)

    +
      +

    • saves the model to the specified directory. The model’s configuration is saved to +<dirname>/config.json, while the model’s binary data is (by default) saved in binary form to +<dirname>/model.pkl. Note that if you edit the saved <dirname>/config.json on disk, the changes +will be loaded when you call ModelClass.load(dirname)!

      • +

    • this method heavily exploits the fact that many objects in Merlion are JSON-serializable

      • +
    +
    3. +

  3. ModelClass.load(dirname, **kwargs)

    +
    +
      +

    • this class method initializes an instance of ModelClass from the config file saved in +<dirname>/config.json, (overriding any parameters of the config with kwargs where relevant), +loads the remaining binary data into the model object, and returns the fully initialized model.

      • +
    +
    +
    4. +
+

For users who aren’t familiar with the specific details of various models, we provide default models for anomaly +detection and forecasting in merlion.models.defaults.

+

We also provide a ModelFactory which can be used to conveniently instantiate models from their name and a set of +keyword arguments, or to load them directly from disk. For example, we may have the following workflow:

+
from merlion.models.factory import ModelFactory
+from merlion.models.anomaly.windstats import WindStats, WindStatsConfig
+
+# creates the same kind of model in 2 equivalent ways
+model1a = WindStats(WindStatsConfig(wind_sz=60))
+model1b = ModelFactory.create("WindStats", wind_sz=60)
+
+# save the model & load it in 2 equivalent ways
+model1a.save("tmp")
+model2a = WindStats.load("tmp")
+model2b = ModelFactory.load("tmp")
+
+
+

Finally, we support ensembles of models in merlion.models.ensemble.

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

defaults

Default models for anomaly detection & forecasting that balance speed and performance.

factory

Contains the ModelFactory.

base

Contains the base classes for all models.

deep_base

Contains the base classes for all deep learning models.

layers

Base class for layered models.

anomaly

Contains all anomaly detection models.

anomaly.change_point

Contains all change point detection algorithms.

anomaly.forecast_based

Contains all forecaster-based anomaly detectors.

forecast

Contains all forecasting models, including those which support exogenous regressors.

ensemble

Ensembles of models and automated model selection.

automl

Contains all AutoML model variants & some utilities.

utils

Contains various utility files & functions useful for different models.

+ +
+

defaults

+

Default models for anomaly detection & forecasting that balance speed and performance.

+
+
+class merlion.models.defaults.DefaultDetectorConfig(model=None, granularity=None, n_threads=1, model_kwargs=None, transform=None, **kwargs)
+

Bases: LayeredModelConfig

+

Config object for default anomaly detection model.

+
+
Parameters
+
    +

  • model – The model being wrapped, or a dict representing it.

    • +

  • granularity (Optional[str]) – the granularity at which the input time series should +be sampled, e.g. “5min”, “1h”, “1d”, etc.

    • +

  • n_threads (int) – the number of parallel threads to use for relevant models

    • +

  • model_kwargs – Keyword arguments used specifically to initialize the underlying model. Only used if +model is a dict. Will override keys in the model dict if specified.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • kwargs – Any other keyword arguments (e.g. for initializing a base class). If model is a dict, +we will also try to pass these arguments when creating the actual underlying model. However, they will +not override arguments in either the model dict or model_kwargs dict.

    • +
+
+
+
+ +
+
+class merlion.models.defaults.DefaultDetector(config=None, model=None, **kwargs)
+

Bases: LayeredDetector

+

Default anomaly detection model that balances efficiency with performance.

+
+
Parameters
+

config (Optional[LayeredModelConfig]) – model configuration

+
+
+
+
+config_class
+

alias of DefaultDetectorConfig

+
+ +
+
+property granularity
+
+ +
+
+reset()
+

Resets the model’s internal state.

+
+ +
+
+train(train_data, train_config=None, anomaly_labels=None, post_rule_train_config=None)
+

Trains the anomaly detector (unsupervised) and its post-rule (supervised, if labels are given) on train data.

+
+
Parameters
+
    +

  • train_data (TimeSeries) – a TimeSeries of metric values to train the model.

    • +

  • train_config – Additional training configs, if needed. Only required for some models.

    • +

  • anomaly_labels (Optional[TimeSeries]) – a TimeSeries indicating which timestamps are anomalous. Optional.

    • +

  • post_rule_train_config – The config to use for training the model’s post-rule. The model’s default +post-rule train config is used if none is supplied here.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

A TimeSeries of the model’s anomaly scores on the training data.

+
+
+
+ +
+ +
+
+class merlion.models.defaults.DefaultForecasterConfig(model=None, max_forecast_steps=None, target_seq_index=None, granularity=None, model_kwargs=None, transform=None, **kwargs)
+

Bases: LayeredModelConfig

+

Config object for default forecasting model.

+
+
Parameters
+
    +

  • model – The model being wrapped, or a dict representing it.

    • +

  • max_forecast_steps (Optional[int]) – Max # of steps we would like to forecast for.

    • +

  • target_seq_index (Optional[int]) – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to forecast.

    • +

  • granularity (Optional[str]) – the granularity at which the input time series should be sampled, e.g. “5min”, “1d”, etc.

    • +

  • model_kwargs – Keyword arguments used specifically to initialize the underlying model. Only used if +model is a dict. Will override keys in the model dict if specified.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • kwargs – Any other keyword arguments (e.g. for initializing a base class). If model is a dict, +we will also try to pass these arguments when creating the actual underlying model. However, they will +not override arguments in either the model dict or model_kwargs dict.

    • +
+
+
+
+ +
+
+class merlion.models.defaults.DefaultForecaster(config=None, model=None, **kwargs)
+

Bases: LayeredForecaster

+

Default forecasting model that balances efficiency with performance.

+
+
+config_class
+

alias of DefaultForecasterConfig

+
+ +
+
+property supports_exog
+

Whether the model supports exogenous regressors.

+
+ +
+
+property granularity
+
+ +
+
+reset()
+

Resets the model’s internal state.

+
+ +
+
+train(train_data, train_config=None, exog_data=None)
+

Trains the forecaster on the input time series.

+
+
Parameters
+
    +

  • train_data (TimeSeries) – a TimeSeries of metric values to train the model.

    • +

  • train_config – Additional training configs, if needed. Only required for some models.

    • +

  • exog_data – A time series of exogenous variables, sampled at the same time stamps as train_data. +Exogenous variables are known a priori, and they are independent of the variable being forecasted. +Only supported for models which inherit from ForecasterExogBase.

    • +
+
+
Return type
+

Tuple[TimeSeries, Optional[TimeSeries]]

+
+
Returns
+

the model’s prediction on train_data, in the same format as +if you called ForecasterBase.forecast on the time stamps of train_data

+
+
+
+ +
+ +
+
+

factory

+

Contains the ModelFactory.

+
+
+class merlion.models.factory.ModelFactory
+

Bases: object

+
+
+classmethod get_model_class(name)
+
+
Return type
+

Type[ModelBase]

+
+
+
+ +
+
+classmethod create(name, return_unused_kwargs=False, **kwargs)
+
+
Return type
+

Union[ModelBase, Tuple[ModelBase, Dict]]

+
+
+
+ +
+
+classmethod load(name, model_path, **kwargs)
+
+
Return type
+

ModelBase

+
+
+
+ +
+
+classmethod load_bytes(obj, **kwargs)
+
+
Return type
+

ModelBase

+
+
+
+ +
+ +
+
+merlion.models.factory.instantiate_or_copy_model(model)
+
+ +
+
+

base

+

Contains the base classes for all models.

+
+
+class merlion.models.base.Config(transform=None, **kwargs)
+

Bases: object

+

Abstract class which defines a model config.

+
+
Parameters
+

transform (Optional[TransformBase]) – Transformation to pre-process input time series.

+
+
+
+
+filename = 'config.json'
+
+ +
+
+transform: TransformBase = None
+
+ +
+
+dim: Optional[int] = None
+
+ +
+
+to_dict(_skipped_keys=None)
+
+
Returns
+

dict with keyword arguments used to initialize the config class.

+
+
+
+ +
+
+classmethod from_dict(config_dict, return_unused_kwargs=False, dim=None, **kwargs)
+

Constructs a Config from a Python dictionary of parameters.

+
+
Parameters
+
    +

  • config_dict (Dict[str, Any]) – dict that will be used to instantiate this object.

    • +

  • return_unused_kwargs – whether to return any unused keyword args.

    • +

  • dim – the dimension of the time series. handled as a special case.

    • +

  • kwargs – any additional parameters to set (overriding config_dict).

    • +
+
+
Returns
+

Config object initialized from the dict.

+
+
+
+ +
+
+get_unused_kwargs(**kwargs)
+
+ +
+ +
+
+class merlion.models.base.NormalizingConfig(normalize=None, transform=None, **kwargs)
+

Bases: Config

+

Model config where the transform must return normalized values. Applies +additional normalization after the initial data pre-processing transform.

+
+
Parameters
+
    +

  • normalize (Optional[Rescale]) – Pre-trained normalization transformation (optional).

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+
+property full_transform
+

Returns the full transform, including the pre-processing step, lags, and +final mean/variance normalization.

+
+ +
+
+property transform
+
+ +
+ +
+
+class merlion.models.base.ModelBase(config)
+

Bases: object

+

Abstract base class for models.

+
+
+filename = 'model.pkl'
+
+ +
+
+config_class
+

alias of Config

+
+ +
+
+train_data: Optional[TimeSeries] = None
+

The data used to train the model.

+
+ +
+
+reset()
+

Resets the model’s internal state.

+
+ +
+
+property base_model
+

The base model of a base model is itself.

+
+ +
+
+abstract property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+abstract property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+
+property auto_align: bool
+

Whether to ensure that all univariates in the training data are aligned.

+
+ +
+
+property supports_exog
+

Whether the model supports exogenous regressors.

+
+ +
+
+property dim
+
+ +
+
+property transform
+
+
Returns
+

The data pre-processing transform to apply on any time series, +before giving it to the model.

+
+
+
+ +
+
+property timedelta
+
+
Returns
+

the gap (as a pandas.Timedelta or pandas.DateOffset) between data points in the training data

+
+
+
+ +
+
+property last_train_time
+
+
Returns
+

the last time (as a pandas.Timestamp) that the model was trained on

+
+
+
+ +
+
+train_pre_process(train_data)
+

Applies pre-processing steps common for training most models.

+
+
Parameters
+

train_data (TimeSeries) – the original time series of training data

+
+
Return type
+

TimeSeries

+
+
Returns
+

the training data, after any necessary pre-processing has been applied

+
+
+
+ +
+
+transform_time_series(time_series, time_series_prev=None)
+

Applies the model’s pre-processing transform to time_series and time_series_prev.

+
+
Parameters
+
    +

  • time_series (TimeSeries) – The time series

    • +

  • time_series_prev (Optional[TimeSeries]) – A time series of context, immediately preceding time_series. Optional.

    • +
+
+
Return type
+

Tuple[TimeSeries, Optional[TimeSeries]]

+
+
Returns
+

The transformed time_series and time_series_prev.

+
+
+
+ +
+
+abstract train(train_data, train_config=None)
+

Trains the model on the specified time series, optionally with some +additional implementation-specific config options train_config.

+
+
Parameters
+
    +

  • train_data (TimeSeries) – a TimeSeries to use as a training set

    • +

  • train_config – additional configurations (if needed)

    • +
+
+
+
+ +
+
+abstract train_post_process(train_result)
+
+ +
+
+save(dirname, **save_config)
+
+
Parameters
+
    +

  • dirname (str) – directory to save the model & its config

    • +

  • save_config – additional configurations (if needed)

    • +
+
+
+
+ +
+
+classmethod load(dirname, **kwargs)
+
+
Parameters
+
    +

  • dirname (str) – directory to load model (and config) from

    • +

  • kwargs – config params to override manually

    • +
+
+
Returns
+

ModelBase object loaded from file

+
+
+
+ +
+
+to_bytes(**save_config)
+

Converts the entire model state and configuration to a single byte object.

+
+
Returns
+

bytes object representing the model.

+
+
+
+ +
+
+classmethod from_bytes(obj, **kwargs)
+

Creates a fully specified model from a byte object

+
+
Parameters
+

obj – byte object to convert into a model

+
+
Returns
+

ModelBase object loaded from obj

+
+
+
+ +
+ +
+
+class merlion.models.base.MultipleTimeseriesModelMixin
+

Bases: object

+

Abstract mixin for models supporting training on multiple time series.

+
+
+abstract train_multiple(multiple_train_data, train_config=None)
+

Trains the model on multiple time series, optionally with some +additional implementation-specific config options train_config.

+
+
Parameters
+
    +

  • multiple_train_data (List[TimeSeries]) – a list of TimeSeries to use as a training set

    • +

  • train_config – additional configurations (if needed)

    • +
+
+
+
+ +
+ +
+
+

deep_base

+

Contains the base classes for all deep learning models.

+
+
+class merlion.models.deep_base.Optimizer(value)
+

Bases: Enum

+

Optimizers for learning model parameters.

+
+
+Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False, *, foreach=None, maximize=False, capturable=False, differentiable=False, fused=False) = <class 'torch.optim.adam.Adam'>
+
+ +
+
+AdamW(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.01, amsgrad=False, *, maximize=False, foreach=None, capturable=False) = <class 'torch.optim.adamw.AdamW'>
+
+ +
+
+SGD(params, lr=<required parameter>, momentum=0, dampening=0, weight_decay=0, nesterov=False, *, maximize=False, foreach=None, differentiable=False) = <class 'torch.optim.sgd.SGD'>
+
+ +
+
+Adagrad(params, lr=0.01, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10, foreach=None, *, maximize=False) = <class 'torch.optim.adagrad.Adagrad'>
+
+ +
+
+RMSprop(params, lr=0.01, alpha=0.99, eps=1e-08, weight_decay=0, momentum=0, centered=False, foreach=None, maximize=False, differentiable=False) = <class 'torch.optim.rmsprop.RMSprop'>
+
+ +
+ +
+
+class merlion.models.deep_base.LossFunction(value)
+

Bases: Enum

+

Loss functions for learning model parameters.

+
+
+mse(size_average=None, reduce=None, reduction='mean') = <class 'torch.nn.modules.loss.MSELoss'>
+
+ +
+
+l1(size_average=None, reduce=None, reduction='mean') = <class 'torch.nn.modules.loss.L1Loss'>
+
+ +
+
+huber(reduction='mean', delta=1.0) = <class 'torch.nn.modules.loss.HuberLoss'>
+
+ +
+
+guassian_nll(*, full=False, eps=1e-06, reduction='mean') = <class 'torch.nn.modules.loss.GaussianNLLLoss'>
+
+ +
+ +
+
+class merlion.models.deep_base.DeepConfig(batch_size=32, num_epochs=10, optimizer=Optimizer.Adam, loss_fn=LossFunction.mse, clip_gradient=None, use_gpu=True, ts_encoding='h', lr=0.0001, weight_decay=0.0, valid_fraction=0.2, early_stop_patience=None, **kwargs)
+

Bases: Config

+

Config object used to define a deep learning (pytorch) model.

+
+
Parameters
+
    +

  • batch_size (int) – Batch size of a batch for stochastic training of deep models

    • +

  • num_epochs (int) – Total number of epochs for training.

    • +

  • optimizer (Union[str, Optimizer]) – The optimizer for learning the parameters of the deep learning models. The value of optimizer +can be Adam, AdamW, SGD, Adagrad, RMSprop.

    • +

  • loss_fn (Union[str, LossFunction]) – Loss function for optimizing deep learning models. The value of loss_fn can be +mse for l2 loss, l1 for l1 loss, huber for huber loss.

    • +

  • clip_gradient (Optional[float]) – Clipping gradient norm of model parameters before updating. If clip_gradient is None, +then the gradient will not be clipped.

    • +

  • use_gpu (bool) – Whether to use gpu for training deep models. If use_gpu = True while thre is no GPU device, +the model will use CPU for training instead.

    • +

  • ts_encoding (Optional[str]) – whether the timestamp should be encoded to a float vector, which can be used +for training deep learning based time series models; if None, the timestamp is not encoded. +If not None, it represents the frequency for time features encoding options:[s:secondly, t:minutely, h:hourly, +d:daily, b:business days, w:weekly, m:monthly]

    • +

  • lr (float) – Learning rate for optimizing deep learning models.

    • +

  • weight_decay (float) – Weight decay (L2 penalty) (default: 0)

    • +

  • valid_fraction (float) – Fraction of validation set to be split from training data

    • +

  • early_stop_patience (Optional[int]) – Number of epochs with no improvement after which training will be stopped for +early stopping function. If early_stop_patience = None, the training process will not stop early.

    • +

  • transform – Transformation to pre-process input time series.

    • +
+
+
+
+
+property optimizer: Optimizer
+
+ +
+
+property loss_fn: LossFunction
+
+ +
+ +
+
+class merlion.models.deep_base.TorchModel(config)
+

Bases: Module

+

Abstract base class for Pytorch deep learning models

+

Initializes internal Module state, shared by both nn.Module and ScriptModule.

+
+
+abstract forward(past, past_timestamp, future_timestamp, *args, **kwargs)
+

Defines the computation performed at every call.

+

Should be overridden by all subclasses.

+
+

Note

+

Although the recipe for forward pass needs to be defined within +this function, one should call the Module instance afterwards +instead of this since the former takes care of running the +registered hooks while the latter silently ignores them.

+
+
+ +
+
+property device
+
+ +
+ +
+
+class merlion.models.deep_base.DeepModelBase(config)
+

Bases: ModelBase

+

Abstract base class for all deep learning models

+
+
+config_class
+

alias of DeepConfig

+
+ +
+
+deep_model_class
+

alias of TorchModel

+
+ +
+
+to_gpu()
+

Move deep model to GPU

+
+ +
+
+to_cpu()
+

Move deep model to CPU

+
+ +
+ +
+
+

layers

+

Base class for layered models. These are models which act as a wrapper around another model, often with additional +functionality. This is the basis for default models and +AutoML models.

+
+
+class merlion.models.layers.LayeredModelConfig(model, model_kwargs=None, transform=None, **kwargs)
+

Bases: Config

+

Config object for a LayeredModel. See LayeredModel documentation for more details.

+
+
Parameters
+
    +

  • model (Union[ModelBase, Dict]) – The model being wrapped, or a dict representing it.

    • +

  • model_kwargs – Keyword arguments used specifically to initialize the underlying model. Only used if +model is a dict. Will override keys in the model dict if specified.

    • +

  • transform – Transformation to pre-process input time series.

    • +

  • kwargs – Any other keyword arguments (e.g. for initializing a base class). If model is a dict, +we will also try to pass these arguments when creating the actual underlying model. However, they will +not override arguments in either the model dict or model_kwargs dict.

    • +
+
+
+
+
+property base_model
+

The base model at the heart of the full layered model.

+
+ +
+
+to_dict(_skipped_keys=None)
+
+
Returns
+

dict with keyword arguments used to initialize the config class.

+
+
+
+ +
+
+classmethod from_dict(config_dict, return_unused_kwargs=False, dim=None, **kwargs)
+

Constructs a Config from a Python dictionary of parameters.

+
+
Parameters
+
    +

  • config_dict (Dict[str, Any]) – dict that will be used to instantiate this object.

    • +

  • return_unused_kwargs – whether to return any unused keyword args.

    • +

  • dim – the dimension of the time series. handled as a special case.

    • +

  • kwargs – any additional parameters to set (overriding config_dict).

    • +
+
+
Returns
+

Config object initialized from the dict.

+
+
+
+ +
+
+get_unused_kwargs(**kwargs)
+
+ +
+ +
+
+class merlion.models.layers.LayeredModel(config=None, model=None, **kwargs)
+

Bases: ModelBase

+

Abstract class implementing a model which wraps around another internal model.

+

The actual underlying model is stored in model.config.model, and model.model is a property which references +this. This is to allow the model to retain the initializer LayeredModel(config), and to ensure that various +attributes do not become de-synchronized (e.g. if we were to store config.model_config and model.model +separately).

+

We define the base model as the non-layered model at the base of the overall model hierarchy.

+

The layered model is allowed to access any callable attribute of the base model, +e.g. model.set_seasonality(...) resolves to``model.base_model.set_seasonality(…)`` for a SeasonalityModel. +If the base model is a forecaster, the layered model will automatically inherit from ForecasterBase; similarly +for DetectorBase or ForecastingDetectorBase. The abstract methods (forecast and get_anomaly_score) +are overridden to call the underlying model.

+

If the base model is a forecaster, the top-level config model.config does not duplicate attributes of the +underlying forecaster config (e.g. max_forecast_steps or target_seq_index). Instead, +model.config.max_forecast_steps will resolve to model.config.base_model.max_forecast_steps. +As a result, you will only need to specify this parameter once. The same holds true for DetectorConfig attributes +(e.g. threshold or calibrator) when the base model is an anomaly detector.

+
+

Note

+

For the time being, every layer of the model is allowed to have its own transform. However, after the +model is trained, the entire transform will be composed as a single TransformSequence and will be owned by +the base model.

+
+
+
+config_class
+

alias of LayeredModelConfig

+
+ +
+
+property require_even_sampling: bool
+

Whether the model assumes that training data is sampled at a fixed frequency

+
+ +
+
+property require_univariate: bool
+

Whether the model only works with univariate time series.

+
+ +
+
+property model
+
+ +
+
+property base_model
+

The base model of a base model is itself.

+
+ +
+
+property train_data
+
+ +
+
+reset()
+

Resets the model’s internal state.

+
+ +
+
+train_pre_process(train_data, **kwargs)
+

Applies pre-processing steps common for training most models.

+
+
Parameters
+

train_data (TimeSeries) – the original time series of training data

+
+
Return type
+

TimeSeries

+
+
Returns
+

the training data, after any necessary pre-processing has been applied

+
+
+
+ +
+
+train_post_process(train_result, **kwargs)
+
+ +
+ +
+
+class merlion.models.layers.LayeredDetector(config=None, model=None, **kwargs)
+

Bases: LayeredModel, DetectorBase

+

Base class for a layered anomaly detector. Only to be used as a subclass.

+
+
Parameters
+

config (Optional[LayeredModelConfig]) – model configuration

+
+
+
+
+get_anomaly_score(time_series, time_series_prev=None, **kwargs)
+

Returns the model’s predicted sequence of anomaly scores.

+
+
Parameters
+
    +

  • time_series (TimeSeries) – the TimeSeries we wish to predict anomaly scores +for.

    • +

  • time_series_prev (Optional[TimeSeries]) – a TimeSeries immediately preceding +time_series. If given, we use it to initialize the time series +anomaly detection model. Otherwise, we assume that time_series +immediately follows the training data.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

a univariate TimeSeries of anomaly scores

+
+
+
+ +
+ +
+
+class merlion.models.layers.LayeredForecaster(config=None, model=None, **kwargs)
+

Bases: LayeredModel, ForecasterBase

+

Base class for a layered forecaster. Only to be used as a subclass.

+
+
+forecast(time_stamps, time_series_prev=None, **kwargs)
+

Returns the model’s forecast on the timestamps given. If self.transform is specified in the config, the +forecast is a forecast of transformed values by default. To invert the transform and forecast the actual +values of the time series, specify invert_transform = True when specifying the config.

+
+
Parameters
+
    +

  • time_stamps – Either a list of timestamps we wish to forecast for, or the number of steps (int) +we wish to forecast for.

    • +

  • time_series_prev (Optional[TimeSeries]) – a time series immediately preceding time_series. If given, we use it to initialize +the forecaster’s state. Otherwise, we assume that time_series immediately follows the training data.

    • +

  • exog_data – A time series of exogenous variables. Exogenous variables are known a priori, and they are +independent of the variable being forecasted. exog_data must include data for all of time_stamps; +if time_series_prev is given, it must include data for all of time_series_prev.time_stamps as well. +Optional. Only supported for models which inherit from ForecasterExogBase.

    • +

  • return_iqr – whether to return the inter-quartile range for the forecast. +Only supported for models which return error bars.

    • +

  • return_prev – whether to return the forecast for time_series_prev (and its stderr or IQR if relevant), +in addition to the forecast for time_stamps. Only used if time_series_prev is provided.

    • +
+
+
Returns
+

(forecast, stderr) if return_iqr is false, (forecast, lb, ub) otherwise.

+
    +

  • forecast: the forecast for the timestamps given

    • +

  • stderr: the standard error of each forecast value. May be None.

    • +

  • lb: 25th percentile of forecast values for each timestamp

    • +

  • ub: 75th percentile of forecast values for each timestamp

    • +
+

+
+
+
+ +
+ +
+
+class merlion.models.layers.LayeredForecastingDetector(config=None, model=None, **kwargs)
+

Bases: LayeredForecaster, LayeredDetector, ForecastingDetectorBase

+

Base class for a layered forecasting detector. Only to be used as a subclass.

+
+
Parameters
+

config (Optional[LayeredModelConfig]) – model configuration

+
+
+
+ +
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
+ +
+
Versions
+ + + +
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+
+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.models.utils.html b/v2.0.2/merlion.models.utils.html new file mode 100644 index 000000000..1e24bec70 --- /dev/null +++ b/v2.0.2/merlion.models.utils.html @@ -0,0 +1,616 @@ + + + + + + utils — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+ + + +
+

utils

+

Contains various utility files & functions useful for different models.

+ ++++ + + + + + + + + + + + + + + +

time_features

Utils for converting pandas datetime to numerical vectors

rolling_window_dataset

A rolling window dataset

early_stopping

Earlying Stopping

autosarima_utils

Low-level utils for AutoML models.

+
+

utils.time_features

+

Utils for converting pandas datetime to numerical vectors

+
+
+class merlion.models.utils.time_features.TimeFeature
+

Bases: object

+
+ +
+
+class merlion.models.utils.time_features.SecondOfMinute
+

Bases: TimeFeature

+

Second of minute encoded as value between [-0.5, 0.5]

+
+ +
+
+class merlion.models.utils.time_features.MinuteOfHour
+

Bases: TimeFeature

+

Minute of hour encoded as value between [-0.5, 0.5]

+
+ +
+
+class merlion.models.utils.time_features.HourOfDay
+

Bases: TimeFeature

+

Hour of day encoded as value between [-0.5, 0.5]

+
+ +
+
+class merlion.models.utils.time_features.DayOfWeek
+

Bases: TimeFeature

+

Day of week encoded as value between [-0.5, 0.5]

+
+ +
+
+class merlion.models.utils.time_features.DayOfMonth
+

Bases: TimeFeature

+

Day of month encoded as value between [-0.5, 0.5]

+
+ +
+
+class merlion.models.utils.time_features.DayOfYear
+

Bases: TimeFeature

+

Day of year encoded as value between [-0.5, 0.5]

+
+ +
+
+class merlion.models.utils.time_features.MonthOfYear
+

Bases: TimeFeature

+

Month of year encoded as value between [-0.5, 0.5]

+
+ +
+
+class merlion.models.utils.time_features.WeekOfYear
+

Bases: TimeFeature

+

Week of year encoded as value between [-0.5, 0.5]

+
+ +
+
+merlion.models.utils.time_features.time_features_from_frequency_str(freq_str)
+
+
Parameters
+

freq_str (str) – Frequency string of the form [multiple][granularity] such as “12H”, “5min”, “1D” etc.

+
+
Return type
+

List[TimeFeature]

+
+
Returns
+

a list of time features that will be appropriate for the given frequency string.

+
+
+
+ +
+
+merlion.models.utils.time_features.get_time_features(dates, ts_encoding='h')
+

Convert pandas Datetime to numerical vectors that can be used for training

+
+ +
+
+

utils.rolling_window_dataset

+

A rolling window dataset

+
+
+class merlion.models.utils.rolling_window_dataset.RollingWindowDataset(data, target_seq_index, n_past, n_future, exog_data=None, shuffle=False, ts_index=False, batch_size=1, flatten=True, ts_encoding=None, valid_fraction=0.0, validation=False, seed=0)
+

Bases: object

+

A rolling window dataset which returns (past, future) windows for the whole time series. +If ts_index=True is used, a batch size of 1 is employed, and each window returned by the dataset is +(past, future), where past and future are both TimeSeries objects. +If ts_index=False is used (default option, more efficient), each window returned by the dataset is +(past_np, past_time, future_np, future_time):

+
    +

  • past_np is a numpy array with shape (batch_size, n_past * dim) if flatten is True, otherwise +(batch_size, n_past, dim).

    • +

  • past_time is a numpy array of times with shape (batch_size, n_past)

    • +

  • future_np is a numpy array with shape (batch_size, dim) if target_seq_index is None +(autoregressive prediction), or shape (batch_size, n_future) if target_seq_index is specified.

    • +

  • future_time is a numpy array of times with shape (batch_size, n_future)

    • +
+
+
Parameters
+
    +

  • data (Union[TimeSeries, DataFrame]) – time series data in the format of TimeSeries or pandas DataFrame with DatetimeIndex

    • +

  • target_seq_index (Optional[int]) – The index of the univariate (amongst all univariates in a general multivariate time +series) whose value we would like to use for the future labeling. If target_seq_index = None, it implies +that all the sequences are required for the future labeling. In this case, we set n_future = 1 and +use the time series for 1-step autoregressive prediction.

    • +

  • n_past (int) – number of steps for past

    • +

  • n_future (int) – number of steps for future. If target_seq_index = None, we manually set n_future = 1.

    • +

  • exog_data (Union[TimeSeries, DataFrame, None]) – exogenous data to as inputs for the model, but not as outputs to predict. +We assume the future values of exogenous variables are known a priori at test time.

    • +

  • shuffle (bool) – whether the windows of the time series should be shuffled.

    • +

  • ts_index (bool) – keep original TimeSeries internally for all the slicing, and output TimeSeries. +by default, Numpy array will handle the internal data workflow and Numpy array will be the output.

    • +

  • batch_size (Optional[int]) – the number of windows to return in parallel. If None, return the whole dataset.

    • +

  • flatten (bool) – whether the output time series arrays should be flattened to 2 dimensions.

    • +

  • ts_encoding (Optional[str]) – whether the timestamp should be encoded to a float vector, which can be used +for training deep learning based time series models; if None, the timestamp is not encoded. +If not None, it represents the frequency for time features encoding options:[s:secondly, t:minutely, h:hourly, +d:daily, b:business days, w:weekly, m:monthly]

    • +

  • valid_fraction (float) – Fraction of validation set splitted from training data. if valid_fraction = 0 +or valid_fraction = 1, we iterate over the entire dataset.

    • +

  • validation (Optional[bool]) – Whether the data is from the validation set or not. if validation = None, we iterate over +the entire dataset.

    • +
+
+
+
+
+property validation
+

If set False, we only provide access to the training windows; if set True, +we only provide access to the validation windows. if set``None``, we iterate over +the entire dataset.

+
+ +
+
+property seed
+

Set Random seed to perturb the training data

+
+ +
+
+property n_windows
+

Number of total slides windows

+
+ +
+
+property n_valid
+

Number of slides windows in validation set

+
+ +
+
+property n_train
+

Number of slides windows in training set

+
+ +
+
+property n_points
+
+ +
+
+collate_batch(batch)
+
+ +
+ +
+
+

utils.early_stopping

+

Earlying Stopping

+
+
+class merlion.models.utils.early_stopping.EarlyStopping(patience=7, delta=0)
+

Bases: object

+

Early stopping for deep model training

+
+
Parameters
+
    +

  • patience – Number of epochs with no improvement after which training will be stopped.

    • +

  • delta – Minimum change in the monitored quantity to qualify as an improvement, +i.e. an absolute change of less than min_delta, will count as no improvement.

    • +
+
+
+
+
+save_best_state_and_dict(val_loss, model)
+
+ +
+
+load_best_model(model)
+
+ +
+ +
+
+

utils.autosarima_utils

+

Low-level utils for AutoML models.

+
+
+merlion.models.utils.autosarima_utils.diff(x, lag=1, differences=1)
+

Return suitably lagged and iterated differences from the given 1D or 2D array x

+
+ +
+
+merlion.models.utils.autosarima_utils.detect_maxiter_sarima_model(y, d, D, m, method, information_criterion, exog=None, **kwargs)
+

run a zero model with SARIMA(2; d; 2)(1; D; 1) / ARIMA(2; d; 2) determine the optimal maxiter

+
+ +
+
+merlion.models.utils.autosarima_utils.seas_seasonalstationaritytest(x, m)
+

Estimate the strength of seasonal component. The idea can be found in +https://otexts.com/fpp2/seasonal-strength.html +R implementation uses mstl instead of stl to deal with multiple seasonality

+
+ +
+
+merlion.models.utils.autosarima_utils.nsdiffs(x, m, max_D=1, test='seas')
+

Estimate the seasonal differencing order D with statistical test

+

Parameters: +x : the time series to difference +m : the number of seasonal periods +max_D : the maximal number of seasonal differencing order allowed +test: the type of test of seasonality to use to detect seasonal periodicity

+
+ +
+
+merlion.models.utils.autosarima_utils.KPSS_stationaritytest(xx, alpha=0.05)
+

The KPSS test is used with the null hypothesis that +x has a stationary root against a unit-root alternative

+

The KPSS test is used with the null hypothesis that +x has a stationary root against a unit-root alternative. +Then the test returns the least number of differences required to +pass the test at the level alpha

+
+ +
+
+merlion.models.utils.autosarima_utils.ndiffs(x, alpha=0.05, max_d=2, test='kpss')
+

Estimate the differencing order d with statistical test

+

Parameters: +x : the time series to difference +alpha : level of the test, possible values range from 0.01 to 0.1 +max_d : the maximal number of differencing order allowed +test: the type of test of seasonality to use to detect seasonal periodicity

+
+ +
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
+ +
+
Versions
+ + + +
latest
+ + + + +
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+ + + + +
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+ + + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.plot.html b/v2.0.2/merlion.plot.html new file mode 100644 index 000000000..c1cb001b4 --- /dev/null +++ b/v2.0.2/merlion.plot.html @@ -0,0 +1,463 @@ + + + + + + merlion.plot package — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

merlion.plot package

+

Module for visualizing model predictions.

+
+
+merlion.plot.plot_anoms(ax, anomaly_labels)
+

Plots anomalies as pink windows on the matplotlib Axes object ax.

+
+ +
+
+merlion.plot.plot_anoms_plotly(fig, anomaly_labels)
+

Plots anomalies as pink windows on the plotly Figure object fig.

+
+ +
+
+class merlion.plot.Figure(y=None, anom=None, yhat=None, yhat_lb=None, yhat_ub=None, y_prev=None, yhat_prev=None, yhat_prev_lb=None, yhat_prev_ub=None, yhat_color=None)
+

Bases: object

+

Class for visualizing predictions of univariate anomaly detection & forecasting models.

+
+
Parameters
+
    +

  • y (Optional[UnivariateTimeSeries]) – the true value of the time series

    • +

  • anom (Optional[UnivariateTimeSeries]) – anomaly scores returned by a model

    • +

  • yhat (Optional[UnivariateTimeSeries]) – forecast returned by a model

    • +

  • yhat_lb (Optional[UnivariateTimeSeries]) – lower bound on yhat (if model supports uncertainty estimation)

    • +

  • yhat_ub (Optional[UnivariateTimeSeries]) – upper bound on yhat (if model supports uncertainty estimation)

    • +

  • y_prev (Optional[UnivariateTimeSeries]) – portion of time series preceding y

    • +

  • yhat_prev (Optional[UnivariateTimeSeries]) – model’s forecast of y_prev

    • +

  • yhat_prev_lb (Optional[UnivariateTimeSeries]) – lower bound on yhat_prev (if model supports uncertainty estimation)

    • +

  • yhat_prev_ub (Optional[UnivariateTimeSeries]) – upper bound on yhat_prev (if model supports uncertainty estimation)

    • +

  • yhat_color (Optional[str]) – the color in which to plot the forecast

    • +
+
+
+
+
+property t0
+
+
Returns
+

First time being plotted.

+
+
+
+ +
+
+property tf
+
+
Returns
+

Final time being plotted.

+
+
+
+ +
+
+property t_split
+
+
Returns
+

Time splitting train from test.

+
+
+
+ +
+
+get_y()
+

Get all y’s (actual values)

+
+ +
+
+get_yhat()
+

Get all yhat’s (predicted values).

+
+ +
+
+get_yhat_iqr()
+

Get IQR of predicted values.

+
+ +
+
+plot(title=None, metric_name=None, figsize=(1000, 600), ax=None, label_alias=None)
+

Plots the figure in matplotlib.

+
+
Parameters
+
    +

  • title – title of the plot.

    • +

  • metric_name – name of the metric (y axis)

    • +

  • figsize – figure size in pixels

    • +

  • ax – matplotlib axes to add the figure to.

    • +

  • label_alias (Optional[Dict[str, str]]) – dict which maps entities in the figure, +specifically y_hat and anom to their label names.

    • +
+
+
Returns
+

(fig, ax): matplotlib figure & matplotlib axes

+
+
+
+ +
+
+plot_plotly(title=None, metric_name=None, figsize=(1000, 600), label_alias=None)
+

Plots the figure in plotly.

+
+
Parameters
+
    +

  • title – title of the plot.

    • +

  • metric_name – name of the metric (y axis)

    • +

  • figsize – figure size in pixels

    • +

  • label_alias (Optional[Dict[str, str]]) – dict which maps entities in the figure, +specifically y_hat and anom to their label names.

    • +
+
+
Returns
+

plotly figure.

+
+
+
+ +
+ +
+
+class merlion.plot.MTSFigure(y=None, anom=None, yhat=None, yhat_lb=None, yhat_ub=None, y_prev=None, yhat_prev=None, yhat_prev_lb=None, yhat_prev_ub=None, yhat_color=None)
+

Bases: object

+
+
+property t0
+
+ +
+
+property tf
+
+ +
+
+property t_split
+
+ +
+
+get_y()
+

Get all y’s (actual values)

+
+ +
+
+get_yhat()
+

Get all yhat’s (predicted values).

+
+ +
+
+get_yhat_iqr()
+

Get IQR of predicted values.

+
+ +
+
+plot_plotly(title=None, figsize=None)
+

Plots the figure in plotly. +:type title: +:param title: title of the plot. +:type figsize: +:param figsize: figure size in pixels +:return: plotly figure.

+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
+ +
+
Versions
+ + + +
latest
+ + + + +
v2.0.2
+ + + + +
v2.0.1
+ + + + +
v2.0.0
+ + + + +
v1.3.1
+ + + + +
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+ + + + +
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+ + + + +
v1.2.4
+ + + + +
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+ + + + +
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+ + + + +
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+ + + + +
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+ + + + +
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+ + + + +
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+ + + + +
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+ + + + +
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+ + + + +
v1.0.2
+ + + + +
v1.0.1
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+ + +
+ +
+
+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.post_process.html b/v2.0.2/merlion.post_process.html new file mode 100644 index 000000000..54b8fdbf6 --- /dev/null +++ b/v2.0.2/merlion.post_process.html @@ -0,0 +1,844 @@ + + + + + + merlion.post_process package — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

merlion.post_process package

+

This package implements some simple rules to post-process the output of an +anomaly detection model. This includes rules for reshaping a sequence to follow +a standard normal distribution (merlion.post_process.calibrate), sparsifying +a sequence based on a threshold (merlion.post_process.threshold), and composing +together sequences of post-processing rules (merlion.post_process.sequence).

+ ++++ + + + + + + + + + + + + + + + + + +

base

Base class for post-processing rules in Merlion.

factory

Contains the PostRuleFactory.

sequence

Class to compose a sequence of post-rules into a single post-rule.

calibrate

Post-rule to transform anomaly scores to follow a standard normal distribution.

threshold

Rules that use a threshold to sparsify a sequence of anomaly scores.

+
+

merlion.post_process.base

+

Base class for post-processing rules in Merlion.

+
+
+class merlion.post_process.base.PostRuleBase
+

Bases: object

+

Base class for post-processing rules in Merlion. These objects are primarily +for post-processing the sequence of anomaly scores returned by anomaly detection +models. All post-rules are callable objects, and they have a train() method +which may accept additional implementation-specific keyword arguments.

+
+
+to_dict()
+
+ +
+
+classmethod from_dict(state_dict)
+
+ +
+
+abstract train(anomaly_scores)
+
+ +
+ +
+
+

merlion.post_process.factory

+

Contains the PostRuleFactory.

+
+
+class merlion.post_process.factory.PostRuleFactory
+

Bases: object

+
+
+classmethod get_post_rule_class(name)
+
+
Return type
+

Type[PostRuleBase]

+
+
+
+ +
+
+classmethod create(name, **kwargs)
+

Uses the given kwargs to create a post-rule of the given name

+
+
Return type
+

PostRuleBase

+
+
+
+ +
+ +
+
+

merlion.post_process.sequence

+

Class to compose a sequence of post-rules into a single post-rule.

+
+
+class merlion.post_process.sequence.PostRuleSequence(post_rules)
+

Bases: PostRuleBase

+
+
+train(anomaly_scores, **kwargs)
+
+
Return type
+

TimeSeries

+
+
+
+ +
+
+to_dict()
+
+ +
+
+classmethod from_dict(state_dict)
+
+ +
+ +
+
+

merlion.post_process.calibrate

+

Post-rule to transform anomaly scores to follow a standard normal distribution.

+
+
+class merlion.post_process.calibrate.AnomScoreCalibrator(max_score, abs_score=True, anchors=None)
+

Bases: PostRuleBase

+

Learns a monotone function which reshapes an input sequence of anomaly scores, +to follow a standard normal distribution. This makes the anomaly scores from +many diverse models interpretable as z-scores.

+
+
Parameters
+
    +

  • max_score (float) – the maximum possible uncalibrated score

    • +

  • abs_score (bool) – whether to consider the absolute values of the +anomaly scores, rather than the raw value.

    • +

  • anchors (Optional[List[Tuple[float, float]]]) – a sequence of (x, y) pairs mapping an uncalibrated +anomaly score to a calibrated anomaly score. Optional, as this +will be set by AnomScoreCalibrator.train.

    • +
+
+
+
+
+property anchors
+
+ +
+
+train(anomaly_scores, retrain_calibrator=False)
+
+
Parameters
+
    +

  • anomaly_scores (TimeSeries) – TimeSeries of raw anomaly scores that we will use +to train the calibrator.

    • +

  • retrain_calibrator – Whether to re-train the calibrator on a new +sequence of anomaly scores, if it has already been trained once. +In practice, we find better results if this is False.

    • +
+
+
Return type
+

TimeSeries

+
+
+
+ +
+ +
+
+

merlion.post_process.threshold

+

Rules that use a threshold to sparsify a sequence of anomaly scores.

+
+
+class merlion.post_process.threshold.Threshold(alm_threshold=None, abs_score=True)
+

Bases: PostRuleBase

+

Zeroes all anomaly scores whose absolute value is less than the threshold.

+
+
Parameters
+
    +

  • alm_threshold (Optional[float]) – Float describing the anomaly threshold.

    • +

  • abs_score – If ‘True’, consider the absolute value instead of the raw value of score.

    • +
+
+
+
+
+class TSADMetric(value)
+

Bases: Enum

+

Enumeration of evaluation metrics for time series anomaly detection. +For each value, the name is the metric, and the value is a partial +function of form f(ground_truth, predicted, **kwargs)

+
+
+MeanTimeToDetect = functools.partial(<function accumulate_tsad_score>, metric=<function TSADScoreAccumulator.mean_time_to_detect>)
+
+ +
+
+F1 = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.f1>, score_type=<ScoreType.RevisedPointAdjusted: 2>))
+
+ +
+
+Precision = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.precision>, score_type=<ScoreType.RevisedPointAdjusted: 2>))
+
+ +
+
+Recall = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.recall>, score_type=<ScoreType.RevisedPointAdjusted: 2>))
+
+ +
+
+PointwiseF1 = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.f1>, score_type=<ScoreType.Pointwise: 0>))
+
+ +
+
+PointwisePrecision = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.precision>, score_type=<ScoreType.Pointwise: 0>))
+
+ +
+
+PointwiseRecall = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.recall>, score_type=<ScoreType.Pointwise: 0>))
+
+ +
+
+PointAdjustedF1 = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.f1>, score_type=<ScoreType.PointAdjusted: 1>))
+
+ +
+
+PointAdjustedPrecision = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.precision>, score_type=<ScoreType.PointAdjusted: 1>))
+
+ +
+
+PointAdjustedRecall = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.recall>, score_type=<ScoreType.PointAdjusted: 1>))
+
+ +
+
+NABScore = functools.partial(<function accumulate_tsad_score>, metric=<function TSADScoreAccumulator.nab_score>)
+
+ +
+
+NABScoreLowFN = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.nab_score>, fn_weight=2.0))
+
+ +
+
+NABScoreLowFP = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.nab_score>, fp_weight=0.22))
+
+ +
+
+F2 = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.f_beta>, score_type=<ScoreType.RevisedPointAdjusted: 2>, beta=2.0))
+
+ +
+
+F5 = functools.partial(<function accumulate_tsad_score>, metric=functools.partial(<function TSADScoreAccumulator.f_beta>, score_type=<ScoreType.RevisedPointAdjusted: 2>, beta=5.0))
+
+ +
+ +
+
+train(anomaly_scores, anomaly_labels=None, metric=None, unsup_quantile=None, max_early_sec=None, max_delay_sec=None, min_allowed_score=None)
+

If metric is available, generates candidate percentiles: [80, 90, 95, 98, 99, 99.5, 99.9]. +Also considers the user-specified candidate percentile in unsup_quantile. Chooses the best +percentile based on metric.

+

If metric is not provided, uses unsup_quantile to choose the threshold. Otherwise, +uses the default threshold specified in alm_threshold.

+
+
Parameters
+
    +

  • anomaly_scores (TimeSeries) – TimeSeries of anomaly scores returned by the model.

    • +

  • anomaly_labels (Optional[TimeSeries]) – TimeSeries of ground truth anomaly labels.

    • +

  • metric (Optional[TSADMetric]) – Metric used to evaluate the performance of candidate thresholds.

    • +

  • unsup_quantile (Optional[float]) – User-specified quantile to use as a candidate.

    • +

  • max_early_sec – Maximum allowed lead time (in seconds) from a detection +to the start of an anomaly.

    • +

  • max_delay_sec – Maximum allowed delay (in seconds) from the start of an +anomaly and a valid detection.

    • +

  • min_allowed_score – The minimum allowed value of the evaluation +metric. If the best candidate threshold achieves a lower value of the +metric, we retain with the current (default) threshold.

    • +
+
+
Return type
+

TimeSeries

+
+
+
+ +
+
+to_simple_threshold()
+
+ +
+ +
+
+class merlion.post_process.threshold.AggregateAlarms(alm_threshold=None, abs_score=True, min_alm_in_window=2, alm_window_minutes=60, alm_suppress_minutes=120)
+

Bases: Threshold

+

Applies basic post-filtering to a time series of anomaly scores

+
    +

  1. Determine which points are anomalies by comparing the absolute value of +their anomaly score to alm_threshold

    2. +

  2. Only fire an alarm when min_alm_in_window of points (within a window +of alarm_window_minutes minutes) are labeled as anomalies.

    3. +

  3. If there is an alarm, then all alarms for the next alm_suppress_minutes +minutes will be suppressed.

    4. +
+

Return a time series of filtered anomaly scores, where the only non-zero +values are the anomaly scores which were marked as alarms (and not +suppressed).

+
+
Parameters
+
    +

  • alm_threshold (Optional[float]) – Float describing the anomaly threshold.

    • +

  • abs_score – If ‘True’, consider the absolute value instead of the raw value of score.

    • +
+
+
+
+
+threshold_class
+

alias of Threshold

+
+ +
+
+property alm_threshold
+
+ +
+
+property abs_score
+
+ +
+
+property window_secs
+
+ +
+
+property suppress_secs
+
+ +
+
+filter(time_series)
+
+
Return type
+

TimeSeries

+
+
+
+ +
+
+train(anomaly_scores, anomaly_labels=None, metric=None, unsup_quantile=None, max_early_sec=None, max_delay_sec=None, min_allowed_score=None)
+

If metric is available, generates candidate percentiles: [80, 90, 95, 98, 99, 99.5, 99.9]. +Also considers the user-specified candidate percentile in unsup_quantile. Chooses the best +percentile based on metric.

+

If metric is not provided, uses unsup_quantile to choose the threshold. Otherwise, +uses the default threshold specified in alm_threshold.

+
+
Parameters
+
    +

  • anomaly_scores (TimeSeries) – TimeSeries of anomaly scores returned by the model.

    • +

  • anomaly_labels (Optional[TimeSeries]) – TimeSeries of ground truth anomaly labels.

    • +

  • metric (Optional[TSADMetric]) – Metric used to evaluate the performance of candidate thresholds.

    • +

  • unsup_quantile (Optional[float]) – User-specified quantile to use as a candidate.

    • +

  • max_early_sec – Maximum allowed lead time (in seconds) from a detection +to the start of an anomaly.

    • +

  • max_delay_sec – Maximum allowed delay (in seconds) from the start of an +anomaly and a valid detection.

    • +

  • min_allowed_score – The minimum allowed value of the evaluation +metric. If the best candidate threshold achieves a lower value of the +metric, we retain with the current (default) threshold.

    • +
+
+
Return type
+

TimeSeries

+
+
+
+ +
+
+to_simple_threshold()
+
+ +
+ +
+
+merlion.post_process.threshold.get_adaptive_thres(x, hist_gap_thres=None, bin_sz=None)
+

Look for gaps in the histogram of anomaly scores (i.e. histogram bins with +zero items inside them). Set the detection threshold to the avg bin size s.t. +the 2 bins have a gap of hist_gap_thres or more

+
+ +
+
+class merlion.post_process.threshold.AdaptiveThreshold(alm_threshold=None, abs_score=True, bin_sz=10, default_hist_gap_thres=1.2)
+

Bases: Threshold

+

Zeroes all anomaly scores whose absolute value is less than the threshold.

+
+
Parameters
+
    +

  • alm_threshold (Optional[float]) – Float describing the anomaly threshold.

    • +

  • abs_score – If ‘True’, consider the absolute value instead of the raw value of score.

    • +
+
+
+
+
+train(anomaly_scores, anomaly_labels=None, metric=None, unsup_quantile=None, max_early_sec=None, max_delay_sec=None, min_allowed_score=None)
+

If metric is available, generates candidate percentiles: [80, 90, 95, 98, 99, 99.5, 99.9]. +Also considers the user-specified candidate percentile in unsup_quantile. Chooses the best +percentile based on metric.

+

If metric is not provided, uses unsup_quantile to choose the threshold. Otherwise, +uses the default threshold specified in alm_threshold.

+
+
Parameters
+
    +

  • anomaly_scores (TimeSeries) – TimeSeries of anomaly scores returned by the model.

    • +

  • anomaly_labels (Optional[TimeSeries]) – TimeSeries of ground truth anomaly labels.

    • +

  • metric (Optional[TSADMetric]) – Metric used to evaluate the performance of candidate thresholds.

    • +

  • unsup_quantile (Optional[float]) – User-specified quantile to use as a candidate.

    • +

  • max_early_sec – Maximum allowed lead time (in seconds) from a detection +to the start of an anomaly.

    • +

  • max_delay_sec – Maximum allowed delay (in seconds) from the start of an +anomaly and a valid detection.

    • +

  • min_allowed_score – The minimum allowed value of the evaluation +metric. If the best candidate threshold achieves a lower value of the +metric, we retain with the current (default) threshold.

    • +
+
+
Return type
+

TimeSeries

+
+
+
+ +
+ +
+
+class merlion.post_process.threshold.AdaptiveAggregateAlarms(alm_threshold=None, abs_score=True, min_alm_in_window=2, alm_window_minutes=60, alm_suppress_minutes=120, bin_sz=10, default_hist_gap_thres=1.2)
+

Bases: AggregateAlarms

+

Applies basic post-filtering to a time series of anomaly scores

+
    +

  1. Determine which points are anomalies by comparing the absolute value of +their anomaly score to alm_threshold

    2. +

  2. Only fire an alarm when min_alm_in_window of points (within a window +of alarm_window_minutes minutes) are labeled as anomalies.

    3. +

  3. If there is an alarm, then all alarms for the next alm_suppress_minutes +minutes will be suppressed.

    4. +
+

Return a time series of filtered anomaly scores, where the only non-zero +values are the anomaly scores which were marked as alarms (and not +suppressed).

+
+
Parameters
+
    +

  • alm_threshold (Optional[float]) – Float describing the anomaly threshold.

    • +

  • abs_score – If ‘True’, consider the absolute value instead of the raw value of score.

    • +
+
+
+
+
+threshold_class
+

alias of AdaptiveThreshold

+
+ +
+
+property bin_sz
+
+ +
+
+property default_hist_gap_thres
+
+ +
+ +
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
+ +
+
Versions
+ + + +
latest
+ + + + +
v2.0.2
+ + + + +
v2.0.1
+ + + + +
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+
+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.spark.html b/v2.0.2/merlion.spark.html new file mode 100644 index 000000000..5002c6891 --- /dev/null +++ b/v2.0.2/merlion.spark.html @@ -0,0 +1,571 @@ + + + + + + merlion.spark package — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

merlion.spark package

+

This module implements APIs to integrate Merlion with PySpark. The expected use case is to +use distributed computing to train and run inference on multiple time series in parallel.

+

There are two ways to use the PySpark API: directly invoking the Spark apps spark_apps/anomaly.py and +spark_apps/forecast.py from the command line with either python or spark-submit, +or using the Dockerfile to serve a Spark application on a Kubernetes cluster with spark-on-k8s. +To understand the expected arguments for these apps, call python spark_apps/anomaly.py -h or +python spark_apps/forecast.py -h.

+
+

Setting up the spark-on-k8s-operator

+

We will now cover how to serve these Spark apps using the +spark-on-k8s-operator. +For all methods, we expect that you have installed Merlion from source by cloning our +git repo.

+

Next, you need to create a Kubernetes cluster. +For local development, we recommend Minikube. +However, you can also use Kubernetes clusters managed by major cloud providers, e.g. +Google’s GKE or +Amazon’s EKS. Setting up these clusters +is beyond the scope of this document, so we defer to the linked resources.

+

Once your Kubernetes cluster is set up, you need to use Helm to install +the spark-on-k8s-operator. A full quick start guide for the operator can be found +here, +but the key steps are to call

+
$ helm repo add spark-operator https://googlecloudplatform.github.io/spark-on-k8s-operator
+$ kubectl create namespace spark-apps
+$ helm install spark-operator spark-operator/spark-operator \
+  --namespace spark-operator --create-namespace --set sparkJobNamespace=spark-apps
+
+
+

This will create a Kubernetes namespace spark-apps from which all your Spark applications will run, and it will +use Helm to install the spark-on-k8s-operator (which manages all running PySpark apps as Kubernetes custom +resources) in the namespace spark-operator.

+

Then, you can build the provided Dockerfile with docker build -t merlion-spark -f docker/spark-on-k8s/Dockerfile . +from the root directory of Merlion. +If you are using Minikube, make sure to point your shell to Minikube’s Docker daemon with +eval $(minikube -p minikube docker-env) before building the image. +If you are working on the cloud, you will need to publish the built Docker image to the appropriate registry, e.g. +Google’s gcr.io or Amazon’s ECR.

+

If you require any additional Java dependencies (e.g. to communicate with a Google GCS bucket or AWS S3 bucket), +we recommend you obtain the jars locally with a package manager like Maven, +and add a line to the Dockerfile which copies those jars to a specific path, e.g. /opt/spark/extra-jars. +Then, you can update the spec.SparkConf block of your Spark app configuration (see below) as follows:

+
spec:
+  sparkConf:
+    spark.driver.extraClassPath: "local:///opt/spark/extra-jars/*"
+    spark.executor.extraClassPath: "local:///opt/spark/extra-jars/*"
+
+
+
+
+

Specifying a Spark App

+

Once your cluster is set up, you can submit a YAML file specifying your spark application as a Kubernetes custom +resource. We provide templates for both forecasting and anomaly detection in k8s-spec/forecast.yml and +k8s-spec/anomaly.yml respectively. Both of these use the walmart_mini.csv dataset, +which contains the weekly sales of 10 different products at 2 different stores.

+

You can change the Docker image used by changing the spec.image in the YAML file. You can modify the amount of +computational resources allocated to the Spark driver and executor by modifying spec.driver and spec.executor +respectively. The arguments to the main application file (spark_apps/anomaly.py or spark_apps/forecast.py) +are specified as a YAML list under spec.arguments. These should be modified according to your use case. +By adding the appropriate Java dependencies and modifying the spec.sparkConf, you can directly read and write files +on cloud storage buckets. While this topic is beyond the scope of this document, we refer an interested reader to +Spark’s Hadoop config, +Hadoop’s AWS S3 connector, and the +GCS connector for more information.

+

More detailed information about specifying a Spark application can be found in the spark-on-k8s-operator’s detailed +API documentation.

+
+
+

API Documentation

+

The API documentation of Merlion’s PySpark connectors (merlion.spark) is below.

+ ++++ + + + + + + + + +

dataset

Utils for reading & writing pyspark datasets.

pandas_udf

Pyspark pandas UDFs for Merlion functions.

+
+

merlion.spark.dataset

+

Utils for reading & writing pyspark datasets.

+
+
+merlion.spark.dataset.TSID_COL_NAME = '__ts_id'
+

Many functions in this module rely on having a column named TSID_COL_NAME being in the dataset. +This column can be added manually using add_tsid_column, and its addition is handled automatically by read_dataset.

+
+ +
+
+merlion.spark.dataset.read_dataset(spark, path, file_format='csv', time_col=None, index_cols=None, data_cols=None)
+

Reads a time series dataset as a pyspark Dataframe.

+
+
Parameters
+
    +

  • spark (SparkSession) – The current SparkSession.

    • +

  • path (str) – The path at which the dataset is stored.

    • +

  • file_format (str) – The file format the dataset is stored in.

    • +

  • time_col (Optional[str]) – The name of the column which specifies timestamp. If None is provided, it is assumed to be the +first column which is not an index column or pre-specified data column.

    • +

  • index_cols (Optional[List[str]]) – The columns used to index the various time series in the dataset. If None is provided, we +assume the entire dataset is just a single time series.

    • +

  • data_cols (Optional[List[str]]) – The columns we will use for downstream time series tasks. If None is provided, we use all +columns that are not a time or index column.

    • +
+
+
Return type
+

DataFrame

+
+
Returns
+

A pyspark dataframe with columns [time_col, *index_cols, *data_cols, TSID_COL_NAME] (in that order).

+
+
+
+ +
+
+merlion.spark.dataset.write_dataset(df, time_col, path, file_format='csv')
+

Writes the dataset at the specified path.

+
+
Parameters
+
    +

  • df (DataFrame) – The dataframe to save. The dataframe must have a column TSID_COL_NAME +indexing the time series in the dataset (this column is automatically added by read_dataset).

    • +

  • time_col (str) – The name of the column which specifies timestamp.

    • +

  • path (str) – The path to save the dataset at.

    • +

  • file_format (str) – The file format in which to save the dataset.

    • +
+
+
+
+ +
+
+merlion.spark.dataset.create_hier_dataset(spark, df, time_col=None, index_cols=None, agg_dict=None)
+

Aggregates the time series in the dataset & appends them to the original dataset.

+
+
Parameters
+
    +

  • spark (SparkSession) – The current SparkSession.

    • +

  • df (DataFrame) – A pyspark dataframe containing all the data. The dataframe must have a column TSID_COL_NAME +indexing the time series in the dataset (this column is automatically added by read_dataset).

    • +

  • time_col (Optional[str]) – The name of the column which specifies timestamp. If None is provided, it is assumed to be the +first column which is not an index column or pre-specified data column.

    • +

  • index_cols (Optional[List[str]]) – The columns used to index the various time series in the dataset. If None is provided, we +assume the entire dataset is just a single time series. These columns define the levels of the hierarchy. +For example, if each time series represents sales and we have index_cols = ["store", "item"], we will +first aggregate sales for all items sold at a particular store; then we will aggregate sales for all items at +all stores.

    • +

  • agg_dict (Optional[Dict]) – A dictionary used to specify how different data columns should be aggregated. If a data column +is not in the dict, we aggregate using sum by default.

    • +
+
+
Return type
+

Tuple[DataFrame, ndarray]

+
+
Returns
+

The dataset with additional time series corresponding to each level of the hierarchy, as well as a +matrix specifying how the hierarchy is constructed.

+
+
+
+ +
+
+merlion.spark.dataset.add_tsid_column(spark, df, index_cols)
+

Adds the column TSID_COL_NAME to the dataframe, which assigns an integer ID to each time series in the dataset.

+
+
Parameters
+
    +

  • spark (SparkSession) – The current SparkSession.

    • +

  • df (DataFrame) – A pyspark dataframe containing all the data.

    • +

  • index_cols (List[str]) – The columns used to index the various time series in the dataset.

    • +
+
+
Return type
+

DataFrame

+
+
Returns
+

The pyspark dataframe with an additional column TSID_COL_NAME added as the last column.

+
+
+
+ +
+
+

merlion.spark.pandas_udf

+

Pyspark pandas UDFs for Merlion functions. +This module contains pandas UDFs that can be called via pyspark.sql.DataFrame.applyInPandas to run Merlion +forecasting, anomaly detection, and time series reconciliation in parallel.

+
+
+merlion.spark.pandas_udf.forecast(pdf, index_cols, time_col, target_col, time_stamps, model, predict_on_train=False, agg_dict=None)
+

Pyspark pandas UDF for performing forecasting. +Should be called on a pyspark dataframe grouped by time series ID, i.e. by index_cols.

+
+
Parameters
+
    +

  • pdf (DataFrame) – The pandas.DataFrame containing the training data. Should be a single time series.

    • +

  • index_cols (List[str]) – The list of column names used to index all the time series in the dataset. Not used for modeling.

    • +

  • time_col (str) – The name of the column containing the timestamps.

    • +

  • target_col (str) – The name of the column whose value we wish to forecast.

    • +

  • time_stamps (Union[List[int], List[str]]) – The timestamps at which we would like to obtain a forecast.

    • +

  • model (Union[ForecasterBase, dict]) – The model (or model dict) we are using to obtain a forecast.

    • +

  • predict_on_train (bool) – Whether to return the model’s prediction on the training data.

    • +

  • agg_dict (Optional[dict]) – A dictionary used to specify how different data columns should be aggregated. If a non-target +data column is not in agg_dict, we do not model it for aggregated time series.

    • +
+
+
Return type
+

DataFrame

+
+
Returns
+

A pandas.DataFrame with the forecast & its standard error (NaN if the model doesn’t have error bars). +Columns are [*index_cols, time_col, target_col, target_col + "_err"].

+
+
+
+ +
+
+merlion.spark.pandas_udf.anomaly(pdf, index_cols, time_col, train_test_split, model, predict_on_train=False)
+

Pyspark pandas UDF for performing anomaly detection. +Should be called on a pyspark dataframe grouped by time series ID, i.e. by index_cols.

+
+
Parameters
+
    +

  • pdf (DataFrame) – The pandas.DataFrame containing the training and testing data. Should be a single time series.

    • +

  • index_cols (List[str]) – The list of column names used to index all the time series in the dataset. Not used for modeling.

    • +

  • time_col (str) – The name of the column containing the timestamps.

    • +

  • train_test_split (Union[int, str]) – The time at which the testing data starts.

    • +

  • model (Union[DetectorBase, dict]) – The model (or model dict) we are using to predict anomaly scores.

    • +

  • predict_on_train (bool) – Whether to return the model’s prediction on the training data.

    • +
+
+
Return type
+

DataFrame

+
+
Returns
+

A pandas.DataFrame with the anomaly scores on the test data. +Columns are [*index_cols, time_col, "anom_score"].

+
+
+
+ +
+
+merlion.spark.pandas_udf.reconciliation(pdf, hier_matrix, target_col)
+

Pyspark pandas UDF for computing the minimum-trace hierarchical time series reconciliation, as described by +Wickramasuriya et al. 2018. +Should be called on a pyspark dataframe grouped by timestamp. Pyspark implementation of +merlion.utils.hts.minT_reconciliation.

+
+
Parameters
+
    +

  • pdf (DataFrame) – A pandas.DataFrame containing forecasted values & standard errors from m time series at a single +timestamp. Each time series should be indexed by TSID_COL_NAME. +The first n time series (in order of ID) orrespond to leaves of the hierarchy, while the remaining m - n +are weighted sums of the first n. +This dataframe can be produced by calling forecast on the dataframe produced by +merlion.spark.dataset.create_hier_dataset.

    • +

  • hier_matrix (ndarray) – A m-by-n matrix describing how the hierarchy is aggregated. The value of the k-th +time series is np.dot(hier_matrix[k], pdf[:n]). This matrix can be produced by +merlion.spark.dataset.create_hier_dataset.

    • +

  • target_col (str) – The name of the column whose value we wish to forecast.

    • +
+
+
Returns
+

A pandas.DataFrame which replaces the original forecasts & errors with reconciled forecasts & errors.

+
+
+
+

Note

+

Time series series reconciliation is skipped if the given timestamp has missing values for any of the +time series. This can happen for training timestamps if the training time series has missing data and +forecast is called with predict_on_train=true.

+
+
+ +
+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
+ +
+
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v1.0.2
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v1.0.1
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v1.0.0
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.transform.html b/v2.0.2/merlion.transform.html new file mode 100644 index 000000000..a61b05d55 --- /dev/null +++ b/v2.0.2/merlion.transform.html @@ -0,0 +1,1441 @@ + + + + + + merlion.transform package — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
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+ +
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+
+
+ + + +
+

merlion.transform package

+

This package provides a number of useful data pre-processing transforms. Each +transform is a callable object that inherits either from TransformBase or +InvertibleTransformBase.

+

We will introduce the key features of transform objects using the Rescale +class. You may initialize a transform in three ways:

+
from merlion.transform.factory import TransformFactory
+from merlion.transform.normalize import Rescale
+
+# Use the initializer
+transform = Rescale(bias=5.0, scale=3.2)
+
+# Use the class's from_dict() method with the arguments you would normally
+# give to the initializer
+kwargs = dict(bias=5.0, scale=3.2)
+transform = Rescale.from_dict(kwargs)
+
+# Use the TransformFactory with the class's name, and the keyword arguments
+# you would normally give to the inializer
+transform = TransformFactory.create("Rescale", **kwargs)
+
+
+

After initializing a transform, one may use it as follows:

+
transform.train(time_series)              # set any trainable params
+transformed = transform(time_series)      # apply the transform to the time series
+inverted = transform.invert(transformed)  # invert the transform
+state_dict = transform.to_dict()          # serialize to a JSON-compatible dict
+
+
+

Note that transform.invert() is supported even if the transform doesn’t +inherit from InvertibleTransformBase! In this case, transform.invert() +implements a pseudo-inverse that may not recover the original time_series +exactly. Additionally, the dict returned by transform.to_dict() is exactly +the same as the dict expected by the class method TransformCls.from_dict().

+

Base primitives:

+ ++++ + + + + + + + + + + + +

factory

Contains the TransformFactory for instantiating transforms.

base

Transform base classes and the Identity transform.

sequence

Classes to compose (TransformSequence) or stack (TransformStack) multiple transforms.

+

Resampling:

+ ++++ + + + + + + + + +

resample

Transforms that resample the input in time, or stack adjacent observations into vectors.

moving_average

Transforms that compute moving averages and k-step differences.

+

Normalization:

+ ++++ + + + + + + + + +

bound

Transforms that clip the input.

normalize

Transforms that rescale the input or otherwise normalize it.

+

Miscellaneous:

+ ++++ + + + + + +

anomalize

Transforms that inject synthetic anomalies into time series.

+
+

Base primitives

+
+

transform.factory

+

Contains the TransformFactory for instantiating transforms.

+
+
+class merlion.transform.factory.TransformFactory
+

Bases: object

+
+
+classmethod get_transform_class(name)
+
+
Return type
+

Type[TransformBase]

+
+
+
+ +
+
+classmethod create(name, **kwargs)
+
+
Return type
+

TransformBase

+
+
+
+ +
+ +
+
+

transform.base

+

Transform base classes and the Identity transform.

+
+
+class merlion.transform.base.TransformBase
+

Bases: object

+

Abstract class for a callable data pre-processing transform.

+

Subclasses must override the train method (pass if +no training is required) and __call__ method (to implement +the actual transform).

+

Subclasses may also support a pseudo inverse transform (possibly using the +implementation-specific self.inversion_state, which should be set +in __call__). If an inversion state is not required, override the +property requires_inversion_state to return False.

+

Due to possible information loss in the forward pass, the inverse transform +may be not be perfect/proper, and calling TransformBase.invert will result +in a warning. By default, the inverse transform (implemented in +TransformBase._invert) is just the identity.

+
+
Variables
+

inversion_state – Implementation-specific intermediate state that is +used to compute the inverse transform for a particular time series. Only +used if TransformBase.requires_inversion_state is True. The +inversion state is destroyed upon calling TransformBase.invert, +unless the option the option retain_inversion_state=True is +specified. This is to prevent potential user error.

+
+
+
+
+_invert(time_series)
+

Helper method which actually performs the inverse transform +(when possible).

+
+
Parameters
+

time_series (TimeSeries) – Time series to apply the inverse transform to

+
+
Return type
+

TimeSeries

+
+
Returns
+

The (inverse) transformed time series.

+
+
+
+ +
+
+property proper_inversion
+

TransformBase objects do not support a proper inversion.

+
+ +
+
+property requires_inversion_state
+

Indicates whether any state self.inversion_state is required to +invert the transform. Specific to each transform. True by default.

+
+ +
+
+property identity_inversion
+

Indicates whether the inverse applied by this transform is just the identity.

+
+ +
+
+to_dict()
+
+ +
+
+classmethod from_dict(state)
+
+ +
+
+abstract train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+
+invert(time_series, retain_inversion_state=False)
+

Applies the inverse of this transform on the time series.

+
+
Parameters
+
    +

  • time_series (TimeSeries) – The time series on which to apply the inverse +transform.

    • +

  • retain_inversion_state – If an inversion state is required, supply +retain_inversion_state=True to retain the inversion state +even after calling this method. Otherwise, the inversion state will +be set to None after the inversion is applied, to prevent a user +error of accidentally using a stale state.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

The (inverse) transformed time series.

+
+
+
+ +
+ +
+
+class merlion.transform.base.InvertibleTransformBase
+

Bases: TransformBase

+

Abstract class for a callable data pre-processing transform with a proper +inverse.

+

In addition to overriding the train and __call__ methods, subclasses +must also override the InvertibleTransformBase._invert method to +implement the actual inverse transform.

+
+
Variables
+

inversion_state – Implementation-specific intermediate state that is +used to compute the inverse transform for a particular time series. Only +used if TransformBase.requires_inversion_state is True. The +inversion state is destroyed upon calling TransformBase.invert, +unless the option the option retain_inversion_state=True is +specified. This is to prevent potential user error.

+
+
+
+
+abstract _invert(time_series)
+

Helper method which actually performs the inverse transform +(when possible).

+
+
Parameters
+

time_series (TimeSeries) – Time series to apply the inverse transform to

+
+
Return type
+

TimeSeries

+
+
Returns
+

The (inverse) transformed time series.

+
+
+
+ +
+
+property proper_inversion
+

InvertibleTransformBase always supports a proper inversion.

+
+ +
+
+property identity_inversion
+

Indicates whether the inverse applied by this transform is just the identity.

+
+ +
+ +
+
+class merlion.transform.base.Identity
+

Bases: InvertibleTransformBase

+

The identity transformation. Does nothing.

+
+
+property requires_inversion_state
+

False because the identity operation is stateless to invert.

+
+ +
+
+property identity_inversion
+

Indicates whether the inverse applied by this transform is just the identity.

+
+ +
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+ +
+
+

transform.sequence

+

Classes to compose (TransformSequence) or stack (TransformStack) multiple transforms.

+
+
+class merlion.transform.sequence.TransformSequence(transforms)
+

Bases: InvertibleTransformBase

+

Applies a series of data transformations sequentially.

+
+
+property proper_inversion
+

A transform sequence is invertible if and only if all the transforms comprising it are invertible.

+
+ +
+
+property identity_inversion
+

Indicates whether the inverse applied by this transform is just the identity.

+
+ +
+
+property requires_inversion_state
+

False because inversion state is held by individual transforms.

+
+ +
+
+to_dict()
+
+ +
+
+append(transform)
+
+ +
+
+classmethod from_dict(state)
+
+ +
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+
+invert(time_series, retain_inversion_state=False)
+

Applies the inverse of this transform on the time series.

+
+
Parameters
+
    +

  • time_series (TimeSeries) – The time series on which to apply the inverse +transform.

    • +

  • retain_inversion_state – If an inversion state is required, supply +retain_inversion_state=True to retain the inversion state +even after calling this method. Otherwise, the inversion state will +be set to None after the inversion is applied, to prevent a user +error of accidentally using a stale state.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

The (inverse) transformed time series.

+
+
+
+ +
+ +
+
+class merlion.transform.sequence.TransformStack(transforms, *, check_aligned=True)
+

Bases: InvertibleTransformBase

+

Applies a set of data transformations individually to an input time series. +Stacks all of the results into a multivariate time series.

+
+
+property proper_inversion
+

A stacked transform is invertible if and only if at least one of the transforms comprising it are invertible.

+
+ +
+
+property requires_inversion_state
+

True because the inversion state tells us which stacked transform to invert, and which part of the +output time series to apply that inverse to.

+
+ +
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+
+invert(time_series, retain_inversion_state=False)
+

Applies the inverse of this transform on the time series.

+
+
Parameters
+
    +

  • time_series (TimeSeries) – The time series on which to apply the inverse +transform.

    • +

  • retain_inversion_state – If an inversion state is required, supply +retain_inversion_state=True to retain the inversion state +even after calling this method. Otherwise, the inversion state will +be set to None after the inversion is applied, to prevent a user +error of accidentally using a stale state.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

The (inverse) transformed time series.

+
+
+
+ +
+ +
+
+
+

Resampling

+
+

transform.resample

+

Transforms that resample the input in time, or stack adjacent observations +into vectors.

+
+
+class merlion.transform.resample.TemporalResample(granularity=None, origin=None, trainable_granularity=None, remove_non_overlapping=True, aggregation_policy='Mean', missing_value_policy='Interpolate')
+

Bases: TransformBase

+

Defines a policy to temporally resample a time series at a specified granularity. Note that while this transform +does support inversion, the recovered time series may differ from the input due to information loss when resampling.

+

Defines a policy to temporally resample a time series.

+
+
Parameters
+
    +

  • granularity (Union[str, int, float, None]) – The granularity at which we want to resample.

    • +

  • origin (Optional[int]) – The time stamp defining the offset to start at.

    • +

  • trainable_granularity (Optional[bool]) – Whether we will automatically infer the granularity of the time series. +If None (default), it will be trainable only if no granularity is explicitly given.

    • +

  • remove_non_overlapping – If True, we will only keep the portions +of the univariates that overlap with each other. For example, if we +have 3 univariates which span timestamps [0, 3600], [60, 3660], and +[30, 3540], we will only keep timestamps in the range [60, 3540]. If +False, we will keep all timestamps produced by the resampling.

    • +

  • aggregation_policy (Union[str, AggregationPolicy]) – The policy we will use to aggregate multiple values in a window (downsampling).

    • +

  • missing_value_policy (Union[str, MissingValuePolicy]) – The policy we will use to impute missing values (upsampling).

    • +
+
+
+
+
+property requires_inversion_state
+

Indicates whether any state self.inversion_state is required to +invert the transform. Specific to each transform. True by default.

+
+ +
+
+property proper_inversion
+

We treat resampling as a proper inversion to avoid emitting warnings.

+
+ +
+
+property granularity
+
+ +
+
+property aggregation_policy: AggregationPolicy
+
+ +
+
+property missing_value_policy: MissingValuePolicy
+
+ +
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+ +
+
+class merlion.transform.resample.Shingle(size=1, stride=1, multivar_skip=True)
+

Bases: InvertibleTransformBase

+

Stacks adjacent observations into a single vector. Downsamples by the +specified stride (less than or equal to the shingle size) if desired.

+

More concretely, consider an input time series,

+
TimeSeries(
+    UnivariateTimeSeries((t1[0], x1[0]), ..., (t1[m], t1[m])),
+    UnivariateTimeSeries((t2[0], x2[0]), ..., (t2[m], t2[m])),
+)
+
+
+

Applying a shingle of size 3 and stride 2 will yield

+
TimeSeries(
+    UnivariateTimeSeries((t1[0], x1[0]), (t1[2], x1[2]), ..., (t1[m-2], x1[m-2])),
+    UnivariateTimeSeries((t1[1], x1[1]), (t1[3], x1[3]), ..., (t1[m-1], x1[m-1])),
+    UnivariateTimeSeries((t1[2], x1[2]), (t1[4], x1[4]), ..., (t1[m],   x1[m])),
+
+    UnivariateTimeSeries((t2[0], x2[0]), (t2[2], x2[2]), ..., (t2[m-2], x2[m-2])),
+    UnivariateTimeSeries((t2[1], x2[1]), (t2[3], x2[3]), ..., (t2[m-1], x2[m-1])),
+    UnivariateTimeSeries((t2[2], x2[2]), (t2[4], x2[4]), ..., (t2[m],   x2[m])),
+)
+
+
+

If the length of any univariate is not perfectly divisible by the stride, we +will pad it on the left side with the first value in the univariate.

+

Converts the time series into shingle vectors of the appropriate size. +This converts each univariate into a multivariate time series with +size variables.

+
+
Parameters
+
    +

  • size (int) – let x(t) = value_t be the value of the time series at +time index t. Then, the output vector for time index t will be +[x(t - size + 1), ..., x(t - 1), x(t)].

    • +

  • stride (int) – The stride at which the output vectors are downsampled.

    • +

  • multivar_skip – Whether to skip this transform if the transform +is already multivariate.

    • +
+
+
+
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+ +
+
+

transform.moving_average

+

Transforms that compute moving averages and k-step differences.

+
+
+class merlion.transform.moving_average.MovingAverage(n_steps=None, weights=None)
+

Bases: InvertibleTransformBase

+

Computes the n_steps-step moving average of the time series, with +the given relative weights assigned to each time in the moving average +(default is to take the non-weighted average). Zero-pads the input time +series to the left before taking the moving average.

+
+
+property requires_inversion_state
+

Indicates whether any state self.inversion_state is required to +invert the transform. Specific to each transform. True by default.

+
+ +
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+ +
+
+class merlion.transform.moving_average.MovingPercentile(n_steps, q)
+

Bases: TransformBase

+

Computes the n-step moving percentile of the time series. +For datapoints at the start of the time series which are preceded by +fewer than n_steps datapoints, the percentile is computed using only the +available datapoints.

+
+
Parameters
+
    +

  • q (float) – The percentile to use. Between 0 and 100 inclusive.

    • +

  • n_steps (int) – The number of steps to use.

    • +
+
+
+
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+ +
+
+class merlion.transform.moving_average.ExponentialMovingAverage(alpha, normalize=True, p=0.95, ci=False)
+

Bases: InvertibleTransformBase

+

Computes the exponential moving average (normalized or un-normalized) of the +time series, with smoothing factor alpha (lower alpha = more smoothing). +alpha must be between 0 and 1.

+

The unnormalized moving average y of x is computed as

+
+\[\begin{split}\begin{align*} +y_0 & = x_0 \\ +y_i & = (1 - \alpha) \cdot y_{i-1} + \alpha \cdot x_i +\end{align*}\end{split}\]
+

The normalized moving average y of x is computed as

+
+\[y_i = \frac{x_i + (1 - \alpha) x_{i-1} + \ldots + (1 - \alpha)^i x_0} +{1 + (1 - \alpha) + \ldots + (1 - \alpha)^i}\]
+

Upper and lower confidence bounds, l and u, of the exponential moving +average are computed using the exponential moving standard deviation, s, and y as

+
+\[\begin{split}l_i = y_i + z_{\frac{1}{2} (1-p)} \times s_i \\ +u_i = u_o + z_{\frac{1}{2} (1+p)} \times s_i\end{split}\]
+

If condfidence bounds are included, the returned time series will contain +the upper and lower bounds as additional univariates. For example if the +transform is applied to a time series with two univariates “x” and “y”, +the resulting time series will contain univariates with the following names: +“x”, “x_lb”, “x_ub”, “y”, “y_lb”, “y_ub”.

+
+
Parameters
+
    +

  • alpha (float) – smoothing factor to use for exponential weighting.

    • +

  • normalize (bool) – If True, divide by the decaying adjustment in +beginning periods.

    • +

  • p (float) – confidence level to use if returning the upper and lower +bounds of the confidence interval.

    • +

  • ci (bool) – If True, return the the upper and lower confidence bounds +of the the exponential moving average as well.

    • +
+
+
+
+
+property requires_inversion_state
+

False because the exponential moving average is stateless to invert.

+
+ +
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+ +
+
+class merlion.transform.moving_average.DifferenceTransform
+

Bases: InvertibleTransformBase

+

Applies a difference transform to the input time series. We include it +as a moving average because we can consider the difference transform +to be a 2-step moving “average” with weights w = [-1, 1].

+
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+ +
+
+class merlion.transform.moving_average.LagTransform(k, pad=False)
+

Bases: InvertibleTransformBase

+

Applies a lag transform to the input time series. Each x(i) gets mapped +to x(i) - x(i-k). We include it as a moving average because we can consider +the lag transform to be a k+1-step moving “average” with weights +w = [-1, 0,…, 0, 1]. One may optionally left-pad the sequence with the +first value in the time series.

+
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+
+compute_lag(var)
+
+
Return type
+

UnivariateTimeSeries

+
+
+
+ +
+ +
+
+
+

Normalization

+
+

transform.normalize

+

Transforms that rescale the input or otherwise normalize it.

+
+
+class merlion.transform.normalize.AbsVal
+

Bases: TransformBase

+

Takes the absolute value of the input time series.

+
+
+property requires_inversion_state
+

False because the “pseudo-inverse” is just the identity (i.e. we lose sign information).

+
+ +
+
+property identity_inversion
+

Indicates whether the inverse applied by this transform is just the identity.

+
+ +
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+ +
+
+class merlion.transform.normalize.Rescale(bias=0.0, scale=1.0, normalize_bias=True, normalize_scale=True)
+

Bases: InvertibleTransformBase

+

Rescales the bias & scale of input vectors or scalars by pre-specified amounts.

+
+
+property requires_inversion_state
+

False because rescaling operations are stateless to invert.

+
+ +
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+
+property is_trained
+
+ +
+ +
+
+class merlion.transform.normalize.MeanVarNormalize(bias=None, scale=None, normalize_bias=True, normalize_scale=True)
+

Bases: Rescale

+

A learnable transform that rescales the values of a time series to have +zero mean and unit variance.

+
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+ +
+
+class merlion.transform.normalize.MinMaxNormalize(bias=None, scale=None, normalize_bias=True, normalize_scale=True)
+

Bases: Rescale

+

A learnable transform that rescales the values of a time series to be +between zero and one.

+
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+ +
+
+class merlion.transform.normalize.BoxCoxTransform(lmbda=None, offset=0.0)
+

Bases: InvertibleTransformBase

+

Applies the Box-Cox power transform to the time series, with power lmbda. +When lmbda is None, we +When lmbda > 0, it is ((x + offset) ** lmbda - 1) / lmbda. +When lmbda == 0, it is ln(lmbda + offset).

+
+
+property requires_inversion_state
+

False because the Box-Cox transform does is stateless to invert.

+
+ +
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+ +
+
+

transform.bound

+

Transforms that clip the input.

+
+
+class merlion.transform.bound.LowerUpperClip(lower=None, upper=None)
+

Bases: TransformBase

+

Clips the values of a time series to lie between lower and upper.

+
+
+property requires_inversion_state
+

False because “inverting” value clipping is stateless.

+
+ +
+
+train(time_series)
+

Sets all trainable parameters of the transform (if any), using the input time series as training data.

+
+ +
+ +
+
+
+

Miscellaneous

+
+

transform.anomalize

+

Transforms that inject synthetic anomalies into time series.

+
+
+class merlion.transform.anomalize.Anomalize(anom_prob=0.01, natural_bounds=(None, None), **kwargs)
+

Bases: TransformBase

+

Injects anomalies into a time series with controlled randomness and returns +both the anomalized time series along with associated anomaly labels.

+
+
Parameters
+
    +

  • anom_prob (float) – The probability of anomalizing a particular data point.

    • +

  • natural_bounds (Tuple[float, float]) – Upper and lower natrual boundaries which injected anomalies should +a particular time series must stay within.

    • +
+
+
+
+
+property natural_bounds
+
+ +
+
+property is_trained: bool
+
+ +
+
+random_is_anom()
+
+ +
+ +
+
+class merlion.transform.anomalize.Shock(alpha=0.2, pos_prob=1.0, sd_range=(3, 6), anom_width_range=(1, 5), persist_shock=False, **kwargs)
+

Bases: Anomalize

+

Injects random spikes or dips into a time series.

+

Letting y_t be a time series, if an anomaly is injected into +the time series at time t, the anomalous value that gets injected is as follows:

+
+\[\begin{split}\tilde{y}_t &= y_t + \text{shock} \\ +\begin{split} +\text{where } \space & \text{shock} = Sign \times Z\times \text{RWSD}_{\alpha}(y_t), \\ +& Z \sim \mathrm{Unif}(a,b), \\ +& Sign \text{ is a random sign} \\ +\end{split}\end{split}\]
+

Additionally, the shock that is added to y_t is also applied to +y_t+1, … y_w-1, where w, known as the “anomaly width” is +randomly determined by a random draw from a uniform distribution.

+
+
Parameters
+
    +

  • alpha (float) – The recency weight to use when calculating recency-weighted +standard deviation.

    • +

  • pos_prob (float) – The probably with which a shock’s sign is positive.

    • +

  • sd_range (Tuple[float, float]) – The range of standard units that is used to create a shock

    • +

  • anom_width_range (Tuple[int, int]) – The range of anomaly widths.

    • +

  • persist_shock (bool) – whether to apply the shock to all successive datapoints.

    • +
+
+
+
+
+property anom_width_range
+
+ +
+
+property sd_range
+
+ +
+
+random_sd_units()
+
+ +
+
+random_anom_width()
+
+ +
+
+random_is_anom()
+
+ +
+
+train(time_series)
+

The Shock transform doesn’t require training.

+
+ +
+ +
+
+class merlion.transform.anomalize.LevelShift(**kwargs)
+

Bases: Shock

+

Injects random level shift anomalies into a time series.

+

A level shift is a sudden change of level in a time series. It is equivalent to +a shock that, when applied to y_t, is also applied to every datapoint after t.

+
+
Parameters
+
    +

  • alpha – The recency weight to use when calculating recency-weighted +standard deviation.

    • +

  • pos_prob – The probably with which a shock’s sign is positive.

    • +

  • sd_range – The range of standard units that is used to create a shock

    • +

  • anom_width_range – The range of anomaly widths.

    • +

  • persist_shock – whether to apply the shock to all successive datapoints.

    • +
+
+
+
+ +
+
+class merlion.transform.anomalize.TrendChange(alpha=0.5, beta=0.95, pos_prob=0.5, scale_range=(0.5, 3.0), **kwargs)
+

Bases: Anomalize

+

Injects random trend changes into a time series.

+

At a high level, the transform tracks the velocity (trend) of a time series +and then, when injecting a trend change at a particular time, it scales +the current velocity by a random factor. The disturbance to the velocity is +persisted to values in the near future, thus emulating a sudden change of trend.

+

Let, (a,b) be the scale range. If the first trend change happens at time t*, +it is injected as follows:

+
+\[\begin{split}\tilde{y}_{t^*} = y_{t^*-1} + v_{t^*} + \Delta v_{t^*} \\ +\begin{align*} +\text{where } & \Delta v_{t^*} = Sign \times Z \times v_{t^*}, \\ +& v_{t^*} = y_{t^*} - y_{t^*-1} +& Z \sim Unif(a,b), \\ +& Sign \text{ is a random sign} \\ +\end{align*}\end{split}\]
+

Afterward, the trend change is persisted and y_t (for t > t*) is changed as follows:

+
+\[\tilde{y}_{t} = \tilde{y}_{t-1} + v_t + \beta \times \Delta v_{t^*}\]
+
+
Parameters
+
    +

  • anom_prob – The probability of anomalizing a particular data point.

    • +

  • natural_bounds – Upper and lower natrual boundaries which injected anomalies should +a particular time series must stay within.

    • +
+
+
+
+
+property scale_range
+
+ +
+
+random_scale()
+
+ +
+
+train(time_series)
+

The TrendChange transform doesn’t require training.

+
+ +
+ +
+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
+ +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/merlion.utils.html b/v2.0.2/merlion.utils.html new file mode 100644 index 000000000..9ee68b05a --- /dev/null +++ b/v2.0.2/merlion.utils.html @@ -0,0 +1,2462 @@ + + + + + + merlion.utils package — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

merlion.utils package

+

This package contains various utilities, including the TimeSeries class and +utilities for resampling time series.

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + +

time_series

Implementation of TimeSeries class.

resample

Code for resampling time series.

data_io

Utils for data I/O.

hts

Aggregation for hierarchical time series.

ts_generator

Generators for synthetic time series.

conj_priors

Implementations of Bayesian conjugate priors & their online update rules.

istat

Incremental computation of time series statistics.

+
+

merlion.utils.time_series

+

Implementation of TimeSeries class.

+
+
+class merlion.utils.time_series.UnivariateTimeSeries(time_stamps, values, name=None, freq='1h')
+

Bases: Series

+

Please read the tutorial before reading this API doc. +This class is a time-indexed pd.Series which represents a univariate +time series. For the most part, it supports all the same features as +pd.Series, with the following key differences to iteration and indexing:

+
    +

  1. Iterating over a UnivariateTimeSeries is implemented as

    +
    for timestamp, value in univariate:
+    # do stuff...
+
    +
    +

    where timestamp is a Unix timestamp, and value is the +corresponding time series value.

    +
    2. +

  2. Integer index: u[i] yields the tuple (u.time_stamps[i], u.values[i])

    3. +

  3. Slice index: u[i:j:k] yields a new +UnivariateTimeSeries(u.time_stamps[i:j:k], u.values[i:j:k])

    4. +
+

The class also supports the following additional features:

+
    +

  1. univariate.time_stamps returns the list of Unix timestamps, and +univariate.values returns the list of the time series values. You +may access the pd.DatetimeIndex directly with univariate.index +(or its np.ndarray representation with univariate.np_time_stamps), +and the np.ndarray of values with univariate.np_values.

    2. +

  2. univariate.concat(other) will concatenate the UnivariateTimeSeries +other to the right end of univariate.

    3. +

  3. left, right = univariate.bisect(t) will split the univariate at the +given timestamp t.

    4. +

  4. window = univariate.window(t0, tf) will return the subset of the time +series occurring between timestamps t0 (inclusive) and tf +(non-inclusive)

    5. +

  5. series = univariate.to_pd() will convert the UnivariateTimeSeries +into a regular pd.Series (for compatibility).

    6. +

  6. univariate = UnivariateTimeSeries.from_pd(series) uses a time-indexed +pd.Series to create a UnivariateTimeSeries object directly.

    7. +
+
+
+__getitem__(i)
+
+
Parameters
+

i (Union[int, slice]) – integer index or slice

+
+
Return type
+

Union[Tuple[float, float], UnivariateTimeSeries]

+
+
Returns
+

(self.time_stamps[i], self.values[i]) if i is +an integer. UnivariateTimeSeries(self.time_series[i], self.values[i]) +if i is a slice.

+
+
+
+ +
+
+__iter__()
+

The i’th item in the iterator is the tuple (self.time_stamps[i], self.values[i]).

+
+ +
+
Parameters
+
    +

  • time_stamps (Optional[Sequence[Union[int, float]]]) – a sequence of Unix timestamps. You may specify +None if you only have values with no specific time stamps.

    • +

  • values (Sequence[float]) – a sequence of univariate values, where values[i] +occurs at time time_stamps[i]

    • +

  • name (Optional[str]) – the name of the univariate time series

    • +

  • freq – if time_stamps is not provided, the univariate is +assumed to be sampled at frequency freq. freq may be a +string (e.g. "1h"), timedelta, or int/float (in units +of seconds).

    • +
+
+
+
+
+property np_time_stamps
+
+
Return type
+

np.ndarray

+
+
Returns
+

the numpy representation of this time series’s Unix timestamps

+
+
+
+ +
+
+property np_values
+
+
Return type
+

np.ndarray

+
+
Returns
+

the numpy representation of this time series’s values

+
+
+
+ +
+
+property time_stamps
+
+
Return type
+

List[float]

+
+
Returns
+

the list of Unix timestamps for the time series

+
+
+
+ +
+
+property values
+
+
Return type
+

List[float]

+
+
Returns
+

the list of values for the time series.

+
+
+
+ +
+
+property t0
+
+
Return type
+

float

+
+
Returns
+

the first timestamp in the univariate time series.

+
+
+
+ +
+
+property tf
+
+
Return type
+

float

+
+
Returns
+

the final timestamp in the univariate time series.

+
+
+
+ +
+
+is_empty()
+
+
Return type
+

bool

+
+
Returns
+

True if the univariate is empty, False if not.

+
+
+
+ +
+
+copy(deep=True)
+

Copies the UnivariateTimeSeries. Simply a wrapper around the +pd.Series.copy() method.

+
+ +
+
+concat(other)
+

Concatenates the UnivariateTimeSeries other to the right of this one. +:param UnivariateTimeSeries other: another UnivariateTimeSeries +:rtype: UnivariateTimeSeries +:return: concatenated univariate time series

+
+ +
+
+bisect(t, t_in_left=False)
+

Splits the time series at the point where the given timestamp occurs.

+
+
Parameters
+
    +

  • t (float) – a Unix timestamp or datetime object. Everything before time +t is in the left split, and everything after time t is in +the right split.

    • +

  • t_in_left (bool) – if True, t is in the left split. Otherwise, +t is in the right split.

    • +
+
+
Return type
+

Tuple[UnivariateTimeSeries, UnivariateTimeSeries]

+
+
Returns
+

the left and right splits of the time series.

+
+
+
+ +
+
+window(t0, tf, include_tf=False)
+
+
Parameters
+
    +

  • t0 (float) – The timestamp/datetime at the start of the window (inclusive)

    • +

  • tf (float) – The timestamp/datetime at the end of the window (inclusive +if include_tf is True, non-inclusive otherwise)

    • +

  • include_tf (bool) – Whether to include tf in the window.

    • +
+
+
Return type
+

UnivariateTimeSeries

+
+
Returns
+

The subset of the time series occurring between timestamps +t0 (inclusive) and tf (included if include_tf is +True, excluded otherwise).

+
+
+
+ +
+
+to_dict()
+
+
Return type
+

Dict[float, float]

+
+
Returns
+

A dictionary representing the data points in the time series.

+
+
+
+ +
+
+classmethod from_dict(obj, name=None)
+
+
Parameters
+
    +

  • obj (Dict[float, float]) – A dictionary of timestamp - value pairs

    • +

  • name – the name to assign the output

    • +
+
+
Return type
+

UnivariateTimeSeries

+
+
Returns
+

the UnivariateTimeSeries represented by series.

+
+
+
+ +
+
+to_pd()
+
+
Return type
+

Series

+
+
Returns
+

A pandas Series representing the time series, indexed by time.

+
+
+
+ +
+
+classmethod from_pd(series, name=None, freq='1h')
+
+
Parameters
+
    +

  • series (Union[Series, DataFrame]) – a pd.Series. If it has a``pd.DatetimeIndex``, we will use that index for the timestamps. +Otherwise, we will create one at the specified frequency.

    • +

  • name – the name to assign the output

    • +

  • freq – if series is not indexed by time, this is the frequency at which we will assume it is sampled.

    • +
+
+
Return type
+

UnivariateTimeSeries

+
+
Returns
+

the UnivariateTimeSeries represented by series.

+
+
+
+ +
+
+to_ts(name=None)
+
+
Name
+

a name to assign the univariate when converting it to a time series. Can override the existing name.

+
+
Return type
+

TimeSeries

+
+
Returns
+

A TimeSeries representing this univariate time series.

+
+
+
+ +
+
+classmethod empty(name=None)
+
+
Return type
+

UnivariateTimeSeries

+
+
Returns
+

A Merlion UnivariateTimeSeries that has empty timestamps and values.

+
+
+
+ +
+ +
+
+class merlion.utils.time_series.TimeSeries(univariates, *, freq='1h', check_aligned=True)
+

Bases: object

+

Please read the tutorial before reading this API doc. +This class represents a general multivariate time series as a wrapper around +a number of (optionally named) UnivariateTimeSeries. A TimeSeries object +is initialized as time_series = TimeSeries(univariates), where +univariates is either a list of UnivariateTimeSeries, or a dictionary +mapping string names to their corresponding UnivariateTimeSeries objects.

+

Because the individual univariates need not be sampled at the same times, an +important concept for TimeSeries is alignment. We say that a TimeSeries +is aligned if all of its univariates have observations sampled at the exact +set set of times.

+

One may access the UnivariateTimeSeries comprising this TimeSeries in four ways:

+
    +

  1. Iterate over the individual univariates using

    +
    for var in time_series.univariates:
+    # do stuff with each UnivariateTimeSeries var
+
    +
    +
    2. +

  2. Access an individual UnivariateTimeSeries by name as +time_series.univariates[name]. If you supplied unnamed univariates to +the constructor (i.e. using a list), the name of a univariate will just +be its index in that list.

    3. +

  3. Get the list of each univariate’s name with time_series.names.

    4. +

  4. Iterate over named univariates as

    +
    for name, var in time_series.items():
+    # do stuff
+
    +
    +

    Note that this is equivalent to iterating over +zip(time_series.names, time_series.univariates).

    +
    5. +
+

This class supports the following additional features as well:

+
    +

  1. Interoperability with pandas

    +
      +

    • df = time_series.to_pd() yields a time-indexed pd.DataFrame, +where each column (with the appropriate name) corresponds to a +variable. Missing values are NaN.

      • +

    • time_series = TimeSeries.from_pd(df) takes a time-indexed +pd.DataFrame and returns a corresponding TimeSeries object +(missing values are handled appropriately). The order of +time_series.univariates is the order of df.keys().

      • +
    +
    2. +

  2. Automated alignment: aligned = time_series.align() resamples each of +time_series.univariates so that they all have the same timestamps. +By default, this is done by taking the union of all timestamps present +in any individual univariate time series, and imputing missing values +via interpolation. See the method documentation for details on how you +may configure the alignment policy.

    3. +

  3. Transparent indexing and iteration for TimeSeries which have all +univariates aligned (i.e. they all have the same timestamps)

    +
      +

    • Get the length and shape of the time series (equal to the number of +observations in each individual univariate). Note that if the time +series is not aligned, we will return the length/shape of an equivalent +pandas dataframe and emit a warning.

      • +

    • Index time_series[i] = (times[i], (x1[i], ..., xn[i])) +(assuming time_series has n aligned univariates with timestamps +times, and xk = time_series.univariates[k-1].values). Slice +returns a TimeSeries object and works as one would expect.

      • +

    • Assuming time_series has n variables, you may iterate with

      +
      for t_i, (x1_i, ..., xn_i) in time_series:
+    # do stuff
+
      +
      +

      Notably, this lets you call times, val_vectors = zip(*time_series)

      +
      • +
    +
    4. +

  4. Time-based queries for any time series

    +
      +

    • Get the two sub TimeSeries before and after a timestamp t via +left, right = time_series.bisect(t)

      • +

    • Get the sub TimeSeries between timestamps t0 (inclusive) and +tf (non-inclusive) via window = time_series.window(t0, tf)

      • +
    +
    5. +

  5. Concatenation: two TimeSeries may be concatenated (in time) as +time_series = time_series_1 + time_series_2.

    6. +
+
+
+__getitem__(i)
+

Only supported if all individual variable time series are sampled at the +same time stamps.

+
+
Parameters
+

i (Union[int, slice]) – integer index or slice.

+
+
Return type
+

Union[Tuple[float, Tuple[float]], TimeSeries]

+
+
Returns
+

If i is an integer, returns the tuple +(time_stamps[i], tuple(var.values[i] for var in self.univariates)). +If i is a slice, returns the time series +TimeSeries([var[i] for var in self.univariates])

+
+
+
+ +
+
+__iter__()
+

Only supported if all individual variable time series are sampled at the +same time stamps. The i’th item of the iterator is the tuple +(time_stamps[i], tuple(var.values[i] for var in self.univariates)).

+
+ +
+
+property names
+
+
Returns
+

The list of the names of the univariates.

+
+
+
+ +
+
+items()
+
+
Returns
+

Iterator over (name, univariate) tuples.

+
+
+
+ +
+
+property dim: int
+
+
Returns
+

The dimension of the time series (the number of variables).

+
+
+
+ +
+
+rename(mapper)
+
+
Parameters
+

mapper (Union[Iterable[str], Mapping[str, str], Callable[[str], str]]) – Dict-like or function transformations to apply to the univariate names. Can also be an iterable +of new univariate names.

+
+
Returns
+

the time series with renamed univariates.

+
+
+
+ +
+
+property is_aligned: bool
+
+
Returns
+

Whether all individual variable time series are sampled at the same time stamps, i.e. they are aligned.

+
+
+
+ +
+
+property index
+
+ +
+
+property np_time_stamps
+
+
Return type
+

np.ndarray

+
+
Returns
+

the numpy representation of this time series’s Unix timestamps

+
+
+
+ +
+
+property time_stamps
+
+
Return type
+

List[float]

+
+
Returns
+

the list of Unix timestamps for the time series

+
+
+
+ +
+
+property t0: float
+
+
Return type
+

float

+
+
Returns
+

the first timestamp in the time series.

+
+
+
+ +
+
+property tf: float
+
+
Return type
+

float

+
+
Returns
+

the final timestamp in the time series.

+
+
+
+ +
+
+is_empty()
+
+
Return type
+

bool

+
+
Returns
+

whether the time series is empty

+
+
+
+ +
+
+squeeze()
+
+
Return type
+

UnivariateTimeSeries

+
+
Returns
+

UnivariateTimeSeries if the time series is univariate; otherwise returns itself, a TimeSeries

+
+
+
+ +
+
+property shape: Tuple[int, int]
+
+
Returns
+

the shape of this time series, i.e. (self.dim, len(self))

+
+
+
+ +
+
+concat(other, axis=0)
+

Concatenates the TimeSeries other on the time axis if axis = 0 or the variable axis if axis = 1. +:rtype: TimeSeries +:return: concatenated time series

+
+ +
+
+bisect(t, t_in_left=False)
+

Splits the time series at the point where the given timestamp t occurs.

+
+
Parameters
+
    +

  • t (float) – a Unix timestamp or datetime object. Everything before time t is in the left split, +and everything after time t is in the right split.

    • +

  • t_in_left (bool) – if True, t is in the left split. Otherwise, t is in the right split.

    • +
+
+
Return type
+

Tuple[TimeSeries, TimeSeries]

+
+
Returns
+

the left and right splits of the time series.

+
+
+
+ +
+
+window(t0, tf, include_tf=False)
+
+
Parameters
+
    +

  • t0 (float) – The timestamp/datetime at the start of the window (inclusive)

    • +

  • tf (float) – The timestamp/datetime at the end of the window (inclusive +if include_tf is True, non-inclusive otherwise)

    • +

  • include_tf (bool) – Whether to include tf in the window.

    • +
+
+
Returns
+

The subset of the time series occurring between timestamps t0 (inclusive) and tf +(included if include_tf is True, excluded otherwise).

+
+
Return type
+

TimeSeries

+
+
+
+ +
+
+to_pd()
+
+
Return type
+

DataFrame

+
+
Returns
+

A pandas DataFrame (indexed by time) which represents this time +series. Each variable corresponds to a column of the DataFrame. +Timestamps which are present for one variable but not another, are +represented with NaN.

+
+
+
+ +
+
+to_csv(file_name, **kwargs)
+
+ +
+
+classmethod from_pd(df, check_times=True, drop_nan=True, freq='1h')
+
+
Parameters
+
    +

  • df (Union[Series, DataFrame, ndarray]) – A pandas.DataFrame with a DatetimeIndex. Each column corresponds to a different variable of +the time series, and the key of column (in sorted order) give the relative order of those variables in +self.univariates. Missing values should be represented with NaN. May also be a pandas.Series +for single-variable time series.

    • +

  • check_times – whether to check that all times in the index are unique (up to the millisecond) and sorted.

    • +

  • drop_nan – whether to drop all NaN entries before creating the time series. Specifying False is +useful if you wish to impute the values on your own.

    • +

  • freq – if df is not indexed by time, this is the frequency at which we will assume it is sampled.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

the TimeSeries object corresponding to df.

+
+
+
+ +
+
+classmethod from_ts_list(ts_list, *, check_aligned=True)
+
+
Parameters
+
    +

  • ts_list (Iterable[TimeSeries]) – iterable of time series we wish to form a multivariate time series with

    • +

  • check_aligned (bool) – whether to check if the output time series is aligned

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

A multivariate TimeSeries created from all the time series in the inputs.

+
+
+
+ +
+
+align(*, reference=None, granularity=None, origin=None, remove_non_overlapping=True, alignment_policy=None, aggregation_policy=AggregationPolicy.Mean, missing_value_policy=MissingValuePolicy.Interpolate)
+

Aligns all the univariates comprising this multivariate time series so that they all have the same time stamps.

+
+
Parameters
+
    +

  • reference (Optional[Sequence[Union[int, float]]]) – A specific set of timestamps we want the resampled time series to contain. Required if +alignment_policy is AlignPolicy.FixedReference. Overrides other alignment policies if specified.

    • +

  • granularity (Union[str, int, float, None]) – The granularity (in seconds) of the resampled time time series. Defaults to the GCD time +difference between adjacent elements of time_series (otherwise). Ignored if reference is given or +alignment_policy is AlignPolicy.FixedReference. Overrides other alignment policies if specified.

    • +

  • origin (Optional[int]) – The first timestamp of the resampled time series. Only used if the alignment policy is +AlignPolicy.FixedGranularity.

    • +

  • remove_non_overlapping – If True, we will only keep the portions of the univariates that overlap with +each other. For example, if we have 3 univariates which span timestamps [0, 3600], [60, 3660], and +[30, 3540], we will only keep timestamps in the range [60, 3540]. If False, we will keep all timestamps +produced by the resampling.

    • +

  • alignment_policy (Optional[AlignPolicy]) –

    The policy we want to use to align the time series.

    +
      +

    • AlignPolicy.FixedReference aligns each single-variable time +series to reference, a user-specified sequence of timestamps.

      • +

    • AlignPolicy.FixedGranularity resamples each single-variable time +series at the same granularity, aggregating windows and imputing +missing values as desired.

      • +

    • AlignPolicy.OuterJoin returns a time series with the union of +all timestamps present in any single-variable time series.

      • +

    • AlignPolicy.InnerJoin returns a time series with the intersection +of all timestamps present in all single-variable time series.

      • +
    +

    • +

  • aggregation_policy (AggregationPolicy) – The policy used to aggregate windows of adjacent observations when downsampling.

    • +

  • missing_value_policy (MissingValuePolicy) – The policy used to impute missing values created when upsampling.

    • +
+
+
Return type
+

TimeSeries

+
+
Returns
+

The resampled multivariate time series.

+
+
+
+ +
+ +
+
+merlion.utils.time_series.assert_equal_timedeltas(time_series, granularity, offset=None)
+

Checks that all time deltas in the time series are equal, either to each +other, or a pre-specified timedelta (in seconds).

+
+ +
+
+

merlion.utils.resample

+

Code for resampling time series.

+
+
+class merlion.utils.resample.AlignPolicy(value)
+

Bases: Enum

+

Policies for aligning multiple univariate time series.

+
+
+OuterJoin = 0
+
+ +
+
+InnerJoin = 1
+
+ +
+
+FixedReference = 2
+
+ +
+
+FixedGranularity = 3
+
+ +
+ +
+
+class merlion.utils.resample.AggregationPolicy(value)
+

Bases: Enum

+

Aggregation policies. Values are partial functions for +pandas.core.resample.Resampler methods.

+
+
+Mean = functools.partial(<function AggregationPolicy.<lambda>>)
+
+ +
+
+Sum = functools.partial(<function AggregationPolicy.<lambda>>)
+
+ +
+
+Median = functools.partial(<function AggregationPolicy.<lambda>>)
+
+ +
+
+First = functools.partial(<function AggregationPolicy.<lambda>>)
+
+ +
+
+Last = functools.partial(<function AggregationPolicy.<lambda>>)
+
+ +
+
+Min = functools.partial(<function AggregationPolicy.<lambda>>)
+
+ +
+
+Max = functools.partial(<function AggregationPolicy.<lambda>>)
+
+ +
+ +
+
+class merlion.utils.resample.MissingValuePolicy(value)
+

Bases: Enum

+

Missing value imputation policies. Values are partial functions for pd.Series methods.

+
+
+FFill = functools.partial(<function MissingValuePolicy.<lambda>>)
+

Fill gap with the first value before the gap.

+
+ +
+
+BFill = functools.partial(<function MissingValuePolicy.<lambda>>)
+

Fill gap with the first value after the gap.

+
+ +
+
+Nearest = functools.partial(<function MissingValuePolicy.<lambda>>, method='nearest')
+

Replace missing value with the value closest to it.

+
+ +
+
+Interpolate = functools.partial(<function MissingValuePolicy.<lambda>>, method='time')
+

Fill in missing values by linear interpolation.

+
+ +
+
+ZFill = functools.partial(<function MissingValuePolicy.<lambda>>, to_replace=nan, value=0)
+

Replace missing values with zeros.

+
+ +
+ +
+
+merlion.utils.resample.to_pd_datetime(timestamp)
+

Converts a timestamp (or list/iterable of timestamps) to pandas Datetime, truncated at the millisecond.

+
+ +
+
+merlion.utils.resample.to_offset(dt)
+

Converts a time gap to a pd.Timedelta if possible, otherwise a pd.DateOffset.

+
+ +
+
+merlion.utils.resample.to_timestamp(t)
+

Converts a datetime to a Unix timestamp.

+
+ +
+
+merlion.utils.resample.granularity_str_to_seconds(granularity)
+

Converts a string/float/int granularity (representing a timedelta) to the +number of seconds it represents, truncated at the millisecond.

+
+
Return type
+

Optional[float]

+
+
+
+ +
+
+merlion.utils.resample.get_date_offset(time_stamps, reference)
+

Returns the date offset one must add to time_stamps so its last timestamp aligns with that of reference.

+
+
Return type
+

DateOffset

+
+
+
+ +
+
+merlion.utils.resample.infer_granularity(time_stamps, return_offset=False)
+

Infers the granularity of a list of time stamps.

+
+ +
+
+merlion.utils.resample.reindex_df(df, reference, missing_value_policy)
+

Reindexes a Datetime-indexed dataframe df to have the same time stamps +as a reference sequence of timestamps. Imputes missing values with the given +MissingValuePolicy.

+
+ +
+
+

merlion.utils.data_io

+

Utils for data I/O.

+
+
+merlion.utils.data_io.df_to_time_series(df, time_col=None, timestamp_unit='s', data_cols=None)
+

Converts a general pandas.DataFrame to a TimeSeries object.

+
+
Parameters
+
    +

  • df (DataFrame) – the dataframe to process

    • +

  • time_col (Optional[str]) – the name of the column specifying time. If None is specified, the existing index +is used if it is a DatetimeIndex. Otherwise, the first column is used.

    • +

  • timestamp_unit – if the time column is in Unix timestamps, this is the unit of the timestamp.

    • +

  • data_cols (Union[str, List[str], None]) – the columns representing the actual data values of interest.

    • +
+
+
Return type
+

TimeSeries

+
+
+
+ +
+
+merlion.utils.data_io.data_io_decorator(func)
+

Decorator to standardize docstrings for data I/O functions.

+
+ +
+
+merlion.utils.data_io.csv_to_time_series(file_name: str, time_col: str = None, timestamp_unit='s', data_cols: Union[str, List[str]] = None) → TimeSeries
+

Reads a CSV file and converts it to a TimeSeries object.

+
+
Parameters
+
    +

  • time_col – the name of the column specifying time. If None is specified, the existing index +is used if it is a DatetimeIndex. Otherwise, the first column is used.

    • +

  • timestamp_unit – if the time column is in Unix timestamps, this is the unit of the timestamp.

    • +

  • data_cols – the columns representing the actual data values of interest.

    • +
+
+
+
+ +
+
+

merlion.utils.hts

+

Aggregation for hierarchical time series.

+
+
+merlion.utils.hts.minT_reconciliation(forecasts, errs, sum_matrix, n_leaves)
+

Computes the minimum trace reconciliation for hierarchical time series, as described by +Wickramasuriya et al. 2018. This algorithm assumes that +we have a number of time series aggregated at various levels (the aggregation tree is described by sum_matrix), +and we obtain independent forecasts at each level of the hierarchy. Minimum trace reconciliation finds the optimal +way to adjust (reconcile) the forecasts to reduce the variance of the estimation.

+
+
Parameters
+
    +

  • forecasts (List[TimeSeries]) – forecast for each aggregation level of the hierarchy

    • +

  • errs (List[TimeSeries]) – standard errors of forecasts for each level of the hierarchy. While not strictly necessary, +reconciliation performs better if all forecasts are accompanied by uncertainty estimates.

    • +

  • sum_matrix (ndarray) – matrix describing how the hierarchy is aggregated

    • +

  • n_leaves (int) – the number of leaf forecasts (i.e. the number of forecasts at the most dis-aggregated level +of the hierarchy). We assume that the leaf forecasts are last in the lists forecasts & errs, +and that sum_matrix reflects this fact.

    • +
+
+
Return type
+

List[TimeSeries]

+
+
Returns
+

reconciled forecasts for each aggregation level of the hierarchy

+
+
+
+ +
+
+

merlion.utils.ts_generator

+

Generators for synthetic time series.

+
+
+class merlion.utils.ts_generator.TimeSeriesGenerator(f, n, x0=0.0, step=1.0, scale=1.0, noise=<built-in method normal of numpy.random.mtrand.RandomState object>, distort=<built-in function add>, name=None, t0='1970 00:00:00', tdelta='5min')
+

Bases: object

+

An abstract base class for generating synthetic time series data. +Generates a 1-dimensional grid x(0), x(1), …, x(n-1), where x(i) = x0 + i * step. +Then generates a time series y(0), y(1), …, y(n-1), where y(i) = f(x(i)) + noise.

+
+
Parameters
+
    +

  • n (int) – The number of points to be generated.

    • +

  • x0 (float) – The initial value to use to form that 1-dimensional grid that +will be used to compute the synthetic values.

    • +

  • step (float) – The step size to use when forming the 1-dimensional grid.

    • +

  • scale (float) – A scalar to use to either inflate or deflate the synthetic data.

    • +

  • noise (Callable[[], float]) – A function that generates a random value when called.

    • +

  • distort (Callable[[float, float], float]) – A function mapping two real numbers to one real number which will +be used to inject noise into the time series.

    • +

  • name (Optional[str]) – The name to assign the univariate that will be generated.

    • +

  • t0 (str) – Initial timestamp to use when wrapping the generated values into a +TimeSeries object.

    • +

  • tdelta (str) – the time delta to use when wrapping the generated values into a +TimeSeries object.

    • +
+
+
+
+
+property n
+
+ +
+
+property x0
+
+ +
+
+property step
+
+ +
+
+y(x)
+
+ +
+
+generate(return_ts=True)
+

Generates synthetic time series data according and returns it as a list or as a +TimeSeries object.

+
+
Return type
+

Union[List[float], TimeSeries]

+
+
+
+ +
+ +
+
+class merlion.utils.ts_generator.GeneratorComposer(generators, per_generator_noise=False, **kwargs)
+

Bases: TimeSeriesGenerator

+

A class for generating synthetic time series by composing +other TimeSeriesGenerator’s.

+
+
Parameters
+
    +

  • n – The number of points to be generated.

    • +

  • x0 – The initial value to use to form that 1-dimensional grid that +will be used to compute the synthetic values.

    • +

  • step – The step size to use when forming the 1-dimensional grid.

    • +

  • scale – A scalar to use to either inflate or deflate the synthetic data.

    • +

  • noise – A function that generates a random value when called.

    • +

  • distort – A function mapping two real numbers to one real number which will +be used to inject noise into the time series.

    • +

  • name – The name to assign the univariate that will be generated.

    • +

  • t0 – Initial timestamp to use when wrapping the generated values into a +TimeSeries object.

    • +

  • tdelta – the time delta to use when wrapping the generated values into a +TimeSeries object.

    • +
+
+
+
+
+property generators
+
+ +
+ +
+
+class merlion.utils.ts_generator.GeneratorConcatenator(string_outputs=True, **kwargs)
+

Bases: GeneratorComposer

+

A class for generating synthetic time series data that undergoes +fundamental changes to it’s behavior that certain points in time. +For example, with this class one could generate a time series that begins +as linear and then becomes stationary.

+

For example, let f = 0 with for 3 steps 0,1,2 and g = 2 * x for the next three +steps 3,4,5. generate() returns:

+
    +

  • [0, 0, 0, 6, 8, 10] if string_outputs is False

    • +

  • [0, 0, 0, 2, 4, 6] if string_outputs is True.

    • +
+
+
param string_outputs: If True, ensure that the end and beginning of each

pair of consecutive time series are connected. For example, Let there be +two generating functions f, and g belonging to consecutive generators. If +True, adjust g by a constant c such that f(x) = g(x) at the last point x +that f uses to generate its series.

+
+
+
+
+property generators
+
+ +
+
+y(x)
+

A Generator Sequence has no method y.

+
+ +
+ +
+
+

merlion.utils.conj_priors

+

Implementations of Bayesian conjugate priors & their online update rules.

+ ++++ + + + + + + + + + + + + + + + + + + + + +

ConjPrior([sample])

Abstract base class for a Bayesian conjugate prior.

BetaBernoulli([sample])

Beta-Bernoulli conjugate prior for binary data.

NormInvGamma([sample])

Normal-InverseGamma conjugate prior.

MVNormInvWishart([sample])

Multivariate Normal-InverseWishart conjugate prior.

BayesianLinReg([sample])

Bayesian Ordinary Linear Regression conjugate prior, which models a univariate input as a function of time.

BayesianMVLinReg([sample])

Bayesian multivariate linear regression conjugate prior, which models a multivariate input as a function of time.

+
+
+class merlion.utils.conj_priors.ConjPrior(sample=None)
+

Bases: ABC

+

Abstract base class for a Bayesian conjugate prior. +Can be used with either TimeSeries or numpy arrays directly.

+
+
Parameters
+

sample – a sample used to initialize the prior.

+
+
+
+
+to_dict()
+
+ +
+
+abstract property n_params: int
+
+ +
+
+classmethod from_dict(state_dict)
+
+ +
+
+static get_time_series_values(x)
+
+
Return type
+

ndarray

+
+
Returns
+

numpy array representing the input x

+
+
+
+ +
+
+process_time_series(x)
+
+
Return type
+

Tuple[ndarray, ndarray]

+
+
Returns
+

(t, x), where t is a normalized list of timestamps, and x is a numpy array +representing the input

+
+
+
+ +
+
+abstract posterior(x, return_rv=False, log=True, return_updated=False)
+

Predictive posterior (log) PDF for new observations, or the scipy.stats random variable where applicable.

+
+
Parameters
+
    +

  • x – value(s) to evaluate posterior at (None implies that we want to return the random variable)

    • +

  • return_rv – whether to return the random variable directly

    • +

  • log – whether to return the log PDF (instead of the PDF)

    • +

  • return_updated – whether to return an updated version of the conjugate prior as well

    • +
+
+
+
+ +
+
+abstract update(x)
+

Update the conjugate prior based on new observations x.

+
+ +
+
+abstract forecast(time_stamps)
+

Return a posterior predictive interval for the time stamps given.

+
+
Parameters
+

time_stamps – a list of time stamps

+
+
Return type
+

Tuple[TimeSeries, TimeSeries]

+
+
Returns
+

(forecast, stderr), where forecast is the expected posterior value and stderr is the +standard error of that forecast.

+
+
+
+ +
+ +
+
+class merlion.utils.conj_priors.ScalarConjPrior(sample=None)
+

Bases: ConjPrior, ABC

+

Abstract base class for a Bayesian conjugate prior for a scalar random variable.

+
+
Parameters
+

sample – a sample used to initialize the prior.

+
+
+
+
+process_time_series(x)
+
+
Returns
+

(t, x), where t is a normalized list of timestamps, and x is a numpy array +representing the input

+
+
+
+ +
+
+static get_time_series_values(x)
+
+
Return type
+

ndarray

+
+
Returns
+

numpy array representing the input x

+
+
+
+ +
+ +
+
+class merlion.utils.conj_priors.BetaBernoulli(sample=None)
+

Bases: ScalarConjPrior

+

Beta-Bernoulli conjugate prior for binary data. We assume the model

+
+\[\begin{split}\begin{align*} +X &\sim \mathrm{Bernoulli}(\theta) \\ +\theta &\sim \mathrm{Beta}(\alpha, \beta) +\end{align*}\end{split}\]
+

The update rule for data \(x_1, \ldots, x_n\) is

+
+\[\begin{split}\begin{align*} +\alpha &= \alpha + \sum_{i=1}^{n} \mathbb{I}[x_i = 1] \\ +\beta &= \beta + \sum_{i=1}^{n} \mathbb{I}[x_i = 0] +\end{align*}\end{split}\]
+
+
Parameters
+

sample – a sample used to initialize the prior.

+
+
+
+
+property n_params: int
+
+ +
+
+posterior(x, return_rv=False, log=True, return_updated=False)
+

The posterior distribution of x is \(\mathrm{Bernoulli}(\alpha / (\alpha + \beta))\).

+
+ +
+
+theta_posterior(theta, return_rv=False, log=True)
+

The posterior distribution of \(\theta\) is \(\mathrm{Beta}(\alpha, \beta)\).

+
+ +
+
+update(x)
+

Update the conjugate prior based on new observations x.

+
+ +
+
+forecast(time_stamps)
+

Return a posterior predictive interval for the time stamps given.

+
+
Parameters
+

time_stamps – a list of time stamps

+
+
Return type
+

Tuple[TimeSeries, TimeSeries]

+
+
Returns
+

(forecast, stderr), where forecast is the expected posterior value and stderr is the +standard error of that forecast.

+
+
+
+ +
+ +
+
+class merlion.utils.conj_priors.NormInvGamma(sample=None)
+

Bases: ScalarConjPrior

+

Normal-InverseGamma conjugate prior. Following +Wikipedia and +Murphy (2007), we assume the model

+
+\[\begin{split}\begin{align*} +X &\sim \mathcal{N}(\mu, \sigma^2) \\ +\mu &\sim \mathcal{N}(\mu_0, \sigma^2 / n) \\ +\sigma^2 &\sim \mathrm{InvGamma}(\alpha, \beta) +\end{align*}\end{split}\]
+

The update rule for data \(x_1, \ldots, x_n\) is

+
+\[\begin{split}\begin{align*} +\bar{x} &= \frac{1}{n} \sum_{i = 1}^{n} x_i \\ +\alpha &= \alpha + n/2 \\ +\beta &= \beta + \frac{1}{2} \sum_{i = 1}^{n} (x_i - \bar{x})^2 + \frac{1}{2} (\mu_0 - \bar{x})^2 \\ +\mu_0 &= \frac{n_0}{n_0 + n} \mu_0 + \frac{n}{n_0 + n} \bar{x} \\ +n_0 &= n_0 + n +\end{align*}\end{split}\]
+
+
Parameters
+

sample – a sample used to initialize the prior.

+
+
+
+
+property n_params: int
+
+ +
+
+update(x)
+

Update the conjugate prior based on new observations x.

+
+ +
+
+mu_posterior(mu, return_rv=False, log=True)
+

The posterior for \(\mu\) is \(\text{Student-t}_{2\alpha}(\mu_0, \beta / (n \alpha))\)

+
+ +
+
+sigma2_posterior(sigma2, return_rv=False, log=True)
+

The posterior for \(\sigma^2\) is \(\text{InvGamma}(\alpha, \beta)\).

+
+ +
+
+posterior(x, log=True, return_rv=False, return_updated=False)
+

The posterior for \(x\) is \(\text{Student-t}_{2\alpha}(\mu_0, (n+1) \beta / (n \alpha))\)

+
+ +
+
+forecast(time_stamps)
+

Return a posterior predictive interval for the time stamps given.

+
+
Parameters
+

time_stamps – a list of time stamps

+
+
Return type
+

Tuple[TimeSeries, TimeSeries]

+
+
Returns
+

(forecast, stderr), where forecast is the expected posterior value and stderr is the +standard error of that forecast.

+
+
+
+ +
+ +
+
+class merlion.utils.conj_priors.MVNormInvWishart(sample=None)
+

Bases: ConjPrior

+

Multivariate Normal-InverseWishart conjugate prior. Multivariate equivalent of Normal-InverseGamma. +Following Murphy (2007), we assume the model

+
+\[\begin{split}\begin{align*} +X &\sim \mathcal{N}_d(\mu, \Sigma) \\ +\mu &\sim \mathcal{N}_d(\mu_0, \Sigma / n) \\ +\Sigma &\sim \mathrm{InvWishart}_{\nu}(\Lambda) +\end{align*}\end{split}\]
+

The update rule for data \(x_1, \ldots, x_n\) is

+
+\[\begin{split}\begin{align*} +\bar{x} &= \frac{1}{n} \sum_{i = 1}^{n} x_i \\ +\nu &= \nu + n/2 \\ +\Lambda &= \Lambda + \frac{n_0 n}{n_0 + n} (\mu_0 - \bar{x}) (\mu_0 - \bar{x})^T + +\sum_{i = 1}^{n} (x_i - \bar{x}) (x_i - \bar{x})^T \\ +\mu_0 &= \frac{n_0}{n_0 + n} \mu_0 + \frac{n}{n_0 + n} \bar{x} \\ +n_0 &= n_0 + n +\end{align*}\end{split}\]
+
+
Parameters
+

sample – a sample used to initialize the prior.

+
+
+
+
+property n_params
+
+ +
+
+process_time_series(x)
+
+
Returns
+

(t, x), where t is a normalized list of timestamps, and x is a numpy array +representing the input

+
+
+
+ +
+
+update(x)
+

Update the conjugate prior based on new observations x.

+
+ +
+
+mu_posterior(mu, return_rv=False, log=True)
+

The posterior for \(\mu\) is \(\text{Student-t}_{\nu-d+1}(\mu_0, \Lambda / (n (\nu - d + 1)))\)

+
+ +
+
+Sigma_posterior(sigma2, return_rv=False, log=True)
+

The posterior for \(\Sigma\) is \(\text{InvWishart}_{\nu}(\Lambda^{-1})\)

+
+ +
+
+posterior(x, return_rv=False, log=True, return_updated=False)
+

The posterior for \(x\) is \(\text{Student-t}_{\nu-d+1}(\mu_0, (n + 1) \Lambda / (n (\nu - d + 1)))\)

+
+ +
+
+forecast(time_stamps, name='forecast')
+

Return a posterior predictive interval for the time stamps given.

+
+
Parameters
+

time_stamps – a list of time stamps

+
+
Return type
+

Tuple[TimeSeries, TimeSeries]

+
+
Returns
+

(forecast, stderr), where forecast is the expected posterior value and stderr is the +standard error of that forecast.

+
+
+
+ +
+ +
+
+class merlion.utils.conj_priors.BayesianLinReg(sample=None)
+

Bases: ConjPrior

+

Bayesian Ordinary Linear Regression conjugate prior, which models a univariate input as a function of time. +Following Wikipedia, we assume the model

+
+\[\begin{split}\begin{align*} +x(t) &\sim \mathcal{N}(m t + b, \sigma^2) \\ +w &\sim \mathcal{N}((m_0, b_0), \sigma^2 \Lambda_0^{-1}) \\ +\sigma^2 &\sim \mathrm{InvGamma}(\alpha, \beta) +\end{align*}\end{split}\]
+

Consider new data \((t_1, x_1), \ldots, (t_n, x_n)\). Let \(T \in \mathbb{R}^{n \times 2}\) be +the matrix obtained by stacking the row vector of times with an all-ones row vector. Let +\(w = (m, b) \in \mathbb{R}^{2}\) be the full weight vector. Let \(x \in \mathbb{R}^{n}\) denote +all observed values. Then we have the update rule

+
+\[\begin{split}\begin{align*} +w_{OLS} &= (T^T T)^{-1} T^T x \\ +\Lambda_n &= \Lambda_0 + T^T T \\ +w_n &= (\Lambda_0 + T^T T)^{-1} (\Lambda_0 w_0 + T^T T w_{OLS}) \\ +\alpha_n &= \alpha_0 + n / 2 \\ +\beta_n &= \beta_0 + \frac{1}{2}(x^T x + w_0^T \Lambda_0 w_0 - w_n^T \Lambda_n w_n) +\end{align*}\end{split}\]
+
+
Parameters
+

sample – a sample used to initialize the prior.

+
+
+
+
+property n_params: int
+
+ +
+
+update(x)
+

Update the conjugate prior based on new observations x.

+
+ +
+
+posterior_explicit(x, return_rv=False, log=True, return_updated=False)
+

Let \(\Lambda_n, \alpha_n, \beta_n\) be the posterior values obtained by updating +the model on data \((t_1, x_1), \ldots, (t_n, x_n)\). The predictive posterior has PDF

+
+\[\begin{align*} +P((t, x)) &= \frac{1}{(2 \pi)^{-n/2}} \sqrt{\frac{\det \Lambda_0}{\det \Lambda_n}} +\frac{\beta_0^{\alpha_0}}{\beta_n^{\alpha_n}}\frac{\Gamma(\alpha_n)}{\Gamma(\alpha_0)} +\end{align*}\]
+
+ +
+
+posterior(x, return_rv=False, log=True, return_updated=False)
+

Naive computation of the posterior using Bayes Rule, i.e.

+
+\[\begin{split}\hat{\sigma}^2 &= \mathbb{E}[\sigma^2] \\ +\hat{w} &= \mathbb{E}[w \mid \sigma^2 = \hat{\sigma}^2] \\ +p(x \mid t) &= \frac{ +p(w = \hat{w}, \sigma^2 = \hat{\sigma}^2) +p(x \mid t, w = \hat{w}, \sigma^2 = \hat{\sigma}^2)}{ +p(w = \hat{w}, \sigma^2 = \hat{\sigma}^2 \mid x, t)}\end{split}\]
+
+ +
+
+forecast(time_stamps)
+

Return a posterior predictive interval for the time stamps given.

+
+
Parameters
+

time_stamps – a list of time stamps

+
+
Return type
+

Tuple[TimeSeries, TimeSeries]

+
+
Returns
+

(forecast, stderr), where forecast is the expected posterior value and stderr is the +standard error of that forecast.

+
+
+
+ +
+ +
+
+class merlion.utils.conj_priors.BayesianMVLinReg(sample=None)
+

Bases: ConjPrior

+

Bayesian multivariate linear regression conjugate prior, which models a multivariate input as a function of time. +Following Wikipedia and +Geisser (1965), we assume the model

+
+\[\begin{split}\begin{align*} +X(t) &\sim \mathcal{N}_{d}(m t + b, \Sigma) \\ +(m, b) &\sim \mathcal{N}_{2d}((m_0, b_0), \Sigma \otimes \Lambda_0^{-1}) \\ +\Sigma &\sim \mathrm{InvWishart}_{\nu}(V_0) \\ +\end{align*}\end{split}\]
+

where \((m, b)\) is the concatenation of the vectors \(m\) and \(b\), +\(\Lambda_0 \in \mathbb{R}^{2 \times 2}\), and \(\otimes\) is the Kronecker product. +Consider new data \((t_1, x_1), \ldots, (t_n, x_n)\). Let \(T \in \mathbb{R}^{n \times 2}\) be +the matrix obtained by stacking the row vector of times with an all-ones row vector. Let +\(W = [m, b]^T \in \mathbb{R}^{2 \times d}\) be the full weight matrix. Let +\(X \in \mathbb{R}^{n \times d}\) be the matrix of observed \(x\) values. Then we have the update rule

+
+\[\begin{split}\begin{align*} +\nu_n &= \nu_0 + n \\ +W_n &= (\Lambda_0 + T^T T)^{-1}(\Lambda_0 W_0 + T^T X) \\ +V_n &= V_0 + (X - TW_n)^T (X - TW_n) + (W_n - W_0)^T \Lambda_0 (W_n - W_0) \\ +\Lambda_n &= \Lambda_0 + T^T T \\ +\end{align*}\end{split}\]
+
+
Parameters
+

sample – a sample used to initialize the prior.

+
+
+
+
+property n_params: int
+
+ +
+
+process_time_series(x)
+
+
Returns
+

(t, x), where t is a normalized list of timestamps, and x is a numpy array +representing the input

+
+
+
+ +
+
+update(x)
+

Update the conjugate prior based on new observations x.

+
+ +
+
+posterior_explicit(x, return_rv=False, log=True, return_updated=False)
+

Let \(\Lambda_n, \nu_n, V_n\) be the posterior values obtained by updating +the model on data \((t_1, x_1), \ldots, (t_n, x_n)\). The predictive posterior has PDF

+
+\[\begin{align*} +P((t, x)) &= \frac{1}{(2 \pi)^{-nd/2}} \sqrt{\frac{\det \Lambda_0}{\det \Lambda_n}} +\frac{\det(V_0/2)^{\nu_0/2}}{\det(V_n/2)^{\nu_n/2}}\frac{\Gamma_d(\nu_n/2)}{\Gamma_d(\nu_0 / 2)} +\end{align*}\]
+
+ +
+
+posterior(x, return_rv=False, log=True, return_updated=False)
+

Naive computation of the posterior using Bayes Rule, i.e.

+
+\[\begin{split}\hat{\Sigma} &= \mathbb{E}[\Sigma] \\ +\hat{W} &= \mathbb{E}[W \mid \Sigma = \hat{\Sigma}] \\ +p(X \mid t) &= \frac{ +p(W = \hat{W}, \Sigma = \hat{\Sigma}) +p(X \mid t, W = \hat{W}, \Sigma = \hat{\Sigma})}{ +p(W = \hat{W}, \Sigma = \hat{\Sigma} \mid x, t)}\end{split}\]
+
+ +
+
+forecast(time_stamps)
+

Return a posterior predictive interval for the time stamps given.

+
+
Parameters
+

time_stamps – a list of time stamps

+
+
Return type
+

Tuple[TimeSeries, TimeSeries]

+
+
Returns
+

(forecast, stderr), where forecast is the expected posterior value and stderr is the +standard error of that forecast.

+
+
+
+ +
+ +
+
+

merlion.utils.istat

+

Incremental computation of time series statistics.

+
+
+class merlion.utils.istat.IStat(value=None, n=0)
+

Bases: object

+

An abstract base class for computing various statistics incrementally, +with emphasis on recency-weighted variants.

+
+
Parameters
+
    +

  • value (Optional[float]) – Initial value of the statistic. Defaults to None.

    • +

  • n (int) – Initial sample size. Defaults to 0.

    • +
+
+
+
+
+property n
+
+ +
+
+property value
+
+ +
+
+abstract add(x)
+

Add a new value to update the statistic. +:type x: +:param x: new value to add to the sample.

+
+ +
+
+abstract drop(x)
+

Drop a value to update the statistic. +:type x: +:param x: value to drop from the sample.

+
+ +
+
+add_batch(batch)
+

Add a batch of new values to update the statistic. +:type batch: List[float] +:param batch: new values to add to the sample.

+
+ +
+
+drop_batch(batch)
+

Drop a batch of new values to update the statistic. +:type batch: List[float] +:param batch: new values to add to the sample.

+
+ +
+ +
+
+class merlion.utils.istat.Mean(value=None, n=0)
+

Bases: IStat

+

Class for incrementally computing the mean of a series of numbers.

+
+
Parameters
+
    +

  • value (Optional[float]) – Initial value of the statistic. Defaults to None.

    • +

  • n (int) – Initial sample size. Defaults to 0.

    • +
+
+
+
+
+property value
+
+ +
+
+add(x)
+

Add a new value to update the statistic. +:type x: +:param x: new value to add to the sample.

+
+ +
+
+drop(x)
+

Drop a value to update the statistic. +:type x: +:param x: value to drop from the sample.

+
+ +
+ +
+
+class merlion.utils.istat.Variance(ex_value=None, ex2_value=None, n=0, ddof=1)
+

Bases: IStat

+

Class for incrementally computing the variance of a series of numbers.

+
+
Parameters
+
    +

  • ex_value (Optional[float]) – Initial value of the first moment (mean).

    • +

  • ex2_value (Optional[float]) – Initial value of the second moment.

    • +

  • n (int) – Initial sample size.

    • +

  • ddof (int) – The delta degrees of freedom to use when correcting +the estimate of the variance.

    • +
+
+
+
+\[\text{Var}(x_i) = \text{E}(x_i^2) - \text{E}(x_i)^2\]
+
+
+mean_class
+

alias of Mean

+
+ +
+
+add(x)
+

Add a new value to update the statistic. +:type x: +:param x: new value to add to the sample.

+
+ +
+
+drop(x)
+

Drop a value to update the statistic. +:type x: +:param x: value to drop from the sample.

+
+ +
+
+property true_value
+
+ +
+
+property corrected_value
+
+ +
+
+property value
+
+ +
+
+property sd
+
+ +
+
+property se
+
+ +
+ +
+
+class merlion.utils.istat.ExponentialMovingAverage(recency_weight=0.1, **kwargs)
+

Bases: Mean

+

Class for incrementally computing the exponential moving average of a series of numbers.

+
+
Parameters
+

recency_weight (float) – Recency weight to use when updating the +exponential moving average.

+
+
+

Letting w be the recency weight,

+
+\[\begin{split}\begin{align*} +\text{EMA}_w(x_0) & = x_0 \\ +\text{EMA}_w(x_t) & = w \cdot x_t + (1-w) \cdot \text{EMA}_w(x_{t-1}) +\end{align*}\end{split}\]
+
+
+property recency_weight
+
+ +
+
+property value
+
+ +
+
+drop(x)
+

Exponential Moving Average does not support dropping values

+
+ +
+ +
+
+class merlion.utils.istat.RecencyWeightedVariance(recency_weight, **kwargs)
+

Bases: Variance

+

Class for incrementally computing the recency-weighted variance of a series of numbers.

+
+
Parameters
+

recency_weight (float) – Recency weight to use when updating the +recency weighted variance.

+
+
+

Letting w be the recency weight,

+
+\[\text{RWV}_w(x_t) = \text{EMA}_w({x^2_t}) - \text{EMA}_w(x_t)^2\]
+
+
+mean_class
+

alias of ExponentialMovingAverage

+
+ +
+
+property recency_weight
+
+ +
+
+drop(x)
+

Recency Weighted Variance does not support dropping values

+
+ +
+ +
+
+ + +
+
+ +
+
+
+
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Data Format", "Tutorial for AutoSARIMA Forecasting Model", "Proof of Concept: Inverse Transforms for Forecasters", "A Gentle Introduction to Anomaly Detection in Merlion", "How to Use Anomaly Detectors in Merlion", "Multivariate Time Series Anomaly Detection", "Adding New Anomaly Detection Models", "A Gentle Introduction to Forecasting in Merlion", "How to Use Forecasters in Merlion", "Multivariate Time Series Forecasting", "Forecasting With Exogenous Regressors", "Adding a New Forecasting Model"], "terms": {"i": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33], "python": [0, 1, 2, 4, 5, 14, 17, 21, 29, 32], "librari": [0, 1, 22, 29, 32], "time": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 25, 26, 28, 29, 30, 32, 33], "seri": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 28, 29, 30, 32, 33], "intellig": 0, "It": [0, 2, 3, 5, 10, 15, 18, 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[[19, "ts_datasets.forecast.SolarPlant"]], "check_ts_for_metadata() (ts_datasets.forecast.customdataset method)": [[19, "ts_datasets.forecast.CustomDataset.check_ts_for_metadata"]], "get_dataset() (in module ts_datasets.forecast)": [[19, "ts_datasets.forecast.get_dataset"]], "metadata_cols (ts_datasets.forecast.customdataset property)": [[19, "ts_datasets.forecast.CustomDataset.metadata_cols"]], "ts_datasets.forecast": [[19, "module-ts_datasets.forecast"]], "url (ts_datasets.forecast.m4 attribute)": [[19, "ts_datasets.forecast.M4.url"]], "valid_subsets (ts_datasets.forecast.m4 attribute)": [[19, "ts_datasets.forecast.M4.valid_subsets"]]}}) \ No newline at end of file diff --git a/v2.0.2/ts_datasets.anomaly.html b/v2.0.2/ts_datasets.anomaly.html new file mode 100644 index 000000000..06e60a419 --- /dev/null +++ b/v2.0.2/ts_datasets.anomaly.html @@ -0,0 +1,601 @@ + + + + + + ts_datasets.anomaly package — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

ts_datasets.anomaly package

+

Datasets for time series anomaly detection (TSAD). All the time series in these +datasets have anomaly labels.

+
+
+ts_datasets.anomaly.get_dataset(dataset_name, rootdir=None, **kwargs)
+
+
Parameters
+
    +

  • dataset_name (str) – the name of the dataset to load, formatted as +<name> or <name>_<subset>, e.g. IOPsCompetition +or NAB_realAWSCloudwatch

    • +

  • rootdir (Optional[str]) – the directory where the desired dataset is stored. Not +required if the package ts_datasets is installed in editable +mode, i.e. with flag -e.

    • +

  • kwargs – keyword arguments for the data loader you are trying to load.

    • +
+
+
Return type
+

TSADBaseDataset

+
+
Returns
+

the data loader for the desired dataset (and subset) desired

+
+
+
+ +
+
+class ts_datasets.anomaly.TSADBaseDataset
+

Bases: BaseDataset

+

Base dataset class for storing time series intended for anomaly detection.

+

Each dataset supports the following features:

+
    +

  1. __getitem__: you may call ts, metadata = dataset[i]. ts is a time-indexed pandas DataFrame, with +each column representing a different variable (in the case of multivariate time series). metadata is a dict or +pd.DataFrame with the same index as ts, with different keys indicating different dataset-specific +metadata (train/test split, anomaly labels, etc.) for each timestamp.

    2. +

  2. __len__: Calling len(dataset) will return the number of time series in the dataset.

    3. +

  3. __iter__: You may iterate over the pandas representations of the time series in the dataset with +for ts, metadata in dataset: ...

    4. +
+
+

Note

+

For each time series, the metadata will always have the key trainval, which is a +pd.Series of bool indicating whether each timestamp of the time series should be +training/validation (if True) or testing (if False).

+
+
+

Note

+

For each time series, the metadata will always have the key anomaly, which is a +pd.Series of bool indicating whether each timestamp is anomalous.

+
+
+
+property max_lead_sec
+

The maximum number of seconds an anomaly may be detected early, for +this dataset. None signifies no early detections allowed, or that +the user may override this value with something better suited for their +purposes.

+
+ +
+
+property max_lag_sec
+

The maximum number of seconds after the start of an anomaly, that we +consider detections to be accurate (and not ignored for being too late). +None signifies that any detection in the window is acceptable, or +that the user may override this value with something better suited for +their purposes.

+
+ +
+
+describe()
+
+ +
+ +
+
+class ts_datasets.anomaly.CustomAnomalyDataset(rootdir, test_frac=0.5, assume_no_anomaly=False, time_col=None, time_unit='s', data_cols=None, index_cols=None)
+

Bases: CustomDataset, TSADBaseDataset

+

Wrapper to load a custom dataset for anomaly detection. Please review the tutorial +to get started.

+
+
Parameters
+
    +

  • rootdir – Filename of a single CSV, or a directory containing many CSVs. Each CSV must contain 1 +or more time series.

    • +

  • test_frac – If we don’t find a column “trainval” in the time series, this is the fraction of each +time series which we use for testing.

    • +

  • assume_no_anomaly – If we don’t find a column “anomaly” in the time series, we assume there are no +anomalies in the data if this value is True, and we throw an exception if this value is False.

    • +

  • time_col – Name of the column used to index time. We use the first non-index, non-metadata column +if none is given.

    • +

  • data_cols – Name of the columns to fetch from the dataset. If None, use all non-time, non-index columns.

    • +

  • time_unit – If the time column is numerical, we assume it is a timestamp expressed in this unit.

    • +

  • index_cols – If a CSV file contains multiple time series, these are the columns used to index those +time series. For example, a CSV file may contain time series of sales for many (store, department) pairs. +In this case, index_cols may be ["Store", "Dept"]. The values of the index columns will be added +to the metadata of the data loader.

    • +
+
+
+
+
+property metadata_cols
+
+ +
+
+check_ts_for_metadata(ts, col)
+
+ +
+ +
+
+class ts_datasets.anomaly.IOpsCompetition(rootdir=None)
+

Bases: TSADBaseDataset

+

Wrapper to load the dataset used for the final round of the IOPs competition +(http://iops.ai/competition_detail/?competition_id=5).

+

The dataset contains 29 time series of KPIs gathered from large tech +companies (Alibaba, Sogou, Tencent, Baidu, and eBay). These time series are +sampled at either 1min or 5min intervals, and are split into train and test +sections.

+

Note that the original competition prohibited algorithms which directly +hard-coded the KPI ID to set model parameters. So training a new model for +each time series was against competition rules. They did, however, allow +algorithms which analyzed each time series (in an automated way), and used +the results of that automated analysis to perform algorithm/model selection.

+
+
Parameters
+

rootdir – The root directory at which the dataset can be found.

+
+
+
+
+property max_lag_sec
+

The IOps competition allows anomalies to be detected up to 35min after +they start. We are currently not using this, but we are leaving the +override here as a placeholder, if we want to change it later.

+
+ +
+ +
+
+class ts_datasets.anomaly.NAB(subset='all', rootdir=None)
+

Bases: TSADBaseDataset

+

Wrapper to load datasets found in the Numenta Anomaly Benchmark +(https://github.com/numenta/NAB).

+

The NAB contains a range of datasets and are categorized by their domains.

+
+
Parameters
+
    +

  • subset – One of the elements in subsets.

    • +

  • rootdir – The root directory at which the dataset can be found.

    • +
+
+
+
+
+valid_subsets = ['all', 'artificial', 'artificialWithAnomaly', 'realAWSCloudwatch', 'realAdExchange', 'realKnownCause', 'realTraffic', 'realTweets']
+
+ +
+
+static load_labels(datafile, label_list, freq)
+
+ +
+
+property max_lead_sec
+

The anomalies in the NAB dataset are already windows which permit early +detection. So we explicitly disallow any earlier detection.

+
+ +
+
+download(rootdir, subsets)
+
+ +
+ +
+
+class ts_datasets.anomaly.Synthetic(subset='anomaly', rootdir=None)
+

Bases: TSADBaseDataset

+

Wrapper to load a sythetically generated dataset. +The dataset was generated using three base time series, each of which +was separately injected with shocks, spikes, dips and level shifts, making +a total of 15 time series (including the base time series without anomalies). +Subsets can are defined by the base time series used (“horizontal”, +“seasonal”, “upward_downward”), or the type of injected anomaly (“shock”, +“spike”, “dip”, “level”). The “anomaly” subset refers to all times series with +injected anomalies (12) while “base” refers to all time series without them (3).

+
+
+base_ts_subsets = ['horizontal', 'seasonal', 'upward_downward']
+
+ +
+
+anomaly_subsets = ['shock', 'spike', 'dip', 'level', 'trend']
+
+ +
+
+valid_subsets = ['anomaly', 'all', 'base', 'horizontal', 'seasonal', 'upward_downward', 'shock', 'spike', 'dip', 'level', 'trend']
+
+ +
+ +
+
+class ts_datasets.anomaly.UCR(rootdir=None)
+

Bases: TSADBaseDataset

+

Data loader for the Hexagon ML/UC Riverside Time Series Anomaly Archive.

+

See here for details.

+

Hoang Anh Dau, Eamonn Keogh, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, +Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Yanping Chen, Bing Hu, +Nurjahan Begum, Anthony Bagnall , Abdullah Mueen, Gustavo Batista, & Hexagon-ML (2019). +The UCR Time Series Classification Archive. URL https://www.cs.ucr.edu/~eamonn/time_series_data_2018/

+
+
+download(rootdir)
+
+ +
+ +
+
+class ts_datasets.anomaly.SMD(subset='all', rootdir=None)
+

Bases: TSADBaseDataset

+

The Server Machine Dataset (SMD) is a new 5-week-long dataset from +a large Internet company collected and made publicly available. +It contains data from 28 server machines and each machine is monitored by 33 metrics. +SMD is divided into training set and testing set of equal size.

+ +
+
+filename = 'ServerMachineDataset'
+
+ +
+
+url = 'https://www.dropbox.com/s/x53ph5cru62kv0f/ServerMachineDataset.tar.gz?dl=1'
+
+ +
+
+valid_subsets = ['machine-1-1', 'machine-1-2', 'machine-1-3', 'machine-1-4', 'machine-1-5', 'machine-1-6', 'machine-1-7', 'machine-1-8', 'machine-2-1', 'machine-2-2', 'machine-2-3', 'machine-2-4', 'machine-2-5', 'machine-2-6', 'machine-2-7', 'machine-2-8', 'machine-2-9', 'machine-3-1', 'machine-3-2', 'machine-3-3', 'machine-3-4', 'machine-3-5', 'machine-3-6', 'machine-3-7', 'machine-3-8', 'machine-3-9', 'machine-3-10', 'machine-3-11']
+
+ +
+ +
+
+class ts_datasets.anomaly.SMAP(subset=None, rootdir=None)
+

Bases: TSADBaseDataset

+

Soil Moisture Active Passive (SMAP) satellite and Mars Science Laboratory (MSL) rover Datasets. +SMAP and MSL are two realworld public datasets, which are two real-world datasets expert-labeled by NASA.

+ +
+
+url = 'https://www.dropbox.com/s/uv9ojw353qwzqht/SMAP.tar.gz?dl=1'
+
+ +
+ +
+
+class ts_datasets.anomaly.MSL(subset=None, rootdir=None)
+

Bases: TSADBaseDataset

+

Soil Moisture Active Passive (SMAP) satellite and Mars Science Laboratory (MSL) rover Datasets. +SMAP and MSL are two realworld public datasets, which are two real-world datasets expert-labeled by NASA.

+ +
+
+url = 'https://www.dropbox.com/s/uv9ojw353qwzqht/SMAP.tar.gz?dl=1'
+
+ +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/ts_datasets.forecast.html b/v2.0.2/ts_datasets.forecast.html new file mode 100644 index 000000000..f5e5461db --- /dev/null +++ b/v2.0.2/ts_datasets.forecast.html @@ -0,0 +1,420 @@ + + + + + + ts_datasets.forecast package — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
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+ + + +
+

ts_datasets.forecast package

+

Datasets for time series forecasting. Really, these are just time series with +no labels of any sort.

+
+
+ts_datasets.forecast.get_dataset(dataset_name, rootdir=None, **kwargs)
+
+
Parameters
+
    +

  • dataset_name (str) – the name of the dataset to load, formatted as +<name> or <name>_<subset>, e.g. EnergyPower or M4_Hourly

    • +

  • rootdir (Optional[str]) – the directory where the desired dataset is stored. Not +required if the package ts_datasets is installed in editable +mode, i.e. with flag -e.

    • +

  • kwargs – keyword arguments for the data loader you are trying to load.

    • +
+
+
Return type
+

BaseDataset

+
+
Returns
+

the data loader for the desired dataset (and subset) desired

+
+
+
+ +
+
+class ts_datasets.forecast.CustomDataset(rootdir, test_frac=0.5, time_col=None, time_unit='s', data_cols=None, index_cols=None)
+

Bases: BaseDataset

+

Wrapper to load a custom dataset. Please review the tutorial to get started.

+
+
Parameters
+
    +

  • rootdir – Filename of a single CSV, or a directory containing many CSVs. Each CSV must contain 1 +or more time series.

    • +

  • test_frac – If we don’t find a column “trainval” in the time series, this is the fraction of each +time series which we use for testing.

    • +

  • time_col – Name of the column used to index time. We use the first non-index, non-metadata column +if none is given.

    • +

  • time_unit – If the time column is numerical, we assume it is a timestamp expressed in this unit.

    • +

  • data_cols – Name of the columns to fetch from the dataset. If None, use all non-time, non-index columns.

    • +

  • index_cols – If a CSV file contains multiple time series, these are the columns used to index those +time series. For example, a CSV file may contain time series of sales for many (store, department) pairs. +In this case, index_cols may be ["Store", "Dept"]. The values of the index columns will be added +to the metadata of the data loader.

    • +
+
+
+
+
+property metadata_cols
+
+ +
+
+check_ts_for_metadata(ts, col)
+
+ +
+ +
+
+class ts_datasets.forecast.M4(subset='Hourly', rootdir=None)
+

Bases: BaseDataset

+

The M4 Competition data is an extended and diverse set of time series to +identify the most accurate forecasting method(s) for different types +of domains, including Business, financial and economic forecasting, +and different type of granularity, including Yearly (23,000 sequences), +Quarterly (24,000 sequences), Monthly (48,000 sequences), +Weekly(359 sequences), Daily (4,227 sequences) and Hourly (414 sequences) +data.

+ +
+
+valid_subsets = ['Yearly', 'Quarterly', 'Monthly', 'Weekly', 'Daily', 'Hourly']
+
+ +
+
+url = 'https://github.com/Mcompetitions/M4-methods/raw/master/Dataset/{}.csv'
+
+ +
+ +
+
+class ts_datasets.forecast.EnergyPower(rootdir=None)
+

Bases: BaseDataset

+

Wrapper to load the open source energy grid power usage dataset.

+ +
+
Parameters
+

rootdir – The root directory at which the dataset can be found.

+
+
+
+ +
+
+class ts_datasets.forecast.SeattleTrail(rootdir=None)
+

Bases: BaseDataset

+

Wrapper to load the open source Seattle Trail pedestrian/bike traffic +dataset.

+ +
+
Parameters
+

rootdir – The root directory at which the dataset can be found.

+
+
+
+ +
+
+class ts_datasets.forecast.SolarPlant(rootdir=None, num_columns=100)
+

Bases: BaseDataset

+

Wrapper to load the open source solar plant power dataset.

+ +
+

Note

+

The loader currently only includes the first 100 (of 405) variables.

+
+
+
Parameters
+
    +

  • rootdir – The root directory at which the dataset can be found.

    • +

  • num_columns – indicates how many univariate columns should be returned

    • +
+
+
+
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+ + +
+ +
+
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+ +
+
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+ + + +
+

ts_datasets: Easy Data Loading

+

ts_datasets implements Python classes that manipulate numerous time series datasets +into standardized pandas.DataFrame s. The sub-modules are ts_datasets.anomaly +for time series anomaly detection, and ts_datasets.forecast for time series forecasting. +Simply install the package by calling pip install -e ts_datasets/ from the root directory of Merlion. +Then, you can load a dataset (e.g. the “realAWSCloudwatch” split of the Numenta Anomaly Benchmark +or the “Hourly” subset of the M4 dataset) by calling

+
from ts_datasets.anomaly import NAB
+from ts_datasets.forecast import M4
+anom_dataset = NAB(subset="realAWSCloudwatch", rootdir=path_to_NAB)
+forecast_dataset = M4(subset="Hourly", rootdir=path_to_M4)
+
+
+

If you install this package in editable mode (i.e. specify -e when calling pip install -e ts_datasets/), +there is no need to specify a rootdir for any of the data loaders.

+

The core features of general data loaders (e.g. for forecasting) are outlined in the API doc for +ts_datasets.base.BaseDataset, and the features for time series anomaly detection data loaders +are outlined in the API doc for ts_datasets.anomaly.TSADBaseDataset.

+

The easiest way to load a custom dataset is to use either the ts_datasets.forecast.CustomDataset or +ts_datasets.anomaly.CustomAnomalyDataset classes. Please review the tutorial +to get started.

+ ++++ + + + + + + + + +

anomaly

Datasets for time series anomaly detection (TSAD).

forecast

Datasets for time series forecasting.

+ +
+

datasets.base module

+
+
+class ts_datasets.base.BaseDataset
+

Bases: object

+

Base dataset class for storing time series as pd.DataFrame s. +Each dataset supports the following features:

+
    +

  1. __getitem__: you may call ts, metadata = dataset[i]. ts is a time-indexed pandas DataFrame, with +each column representing a different variable (in the case of multivariate time series). metadata is a dict or +pd.DataFrame with the same index as ts, with different keys indicating different dataset-specific +metadata (train/test split, anomaly labels, etc.) for each timestamp.

    2. +

  2. __len__: Calling len(dataset) will return the number of time series in the dataset.

    3. +

  3. __iter__: You may iterate over the pandas representations of the time series in the dataset with +for ts, metadata in dataset: ...

    4. +
+
+

Note

+

For each time series, the metadata will always have the key trainval, which is a +pd.Series of bool indicating whether each timestamp of the time series should be +training/validation (if True) or testing (if False).

+
+
+
+time_series: list
+

A list of all individual time series contained in the dataset. Iterating over +the dataset will iterate over this list. Note that for some large datasets, +time_series may be a list of filenames, which are read lazily either during +iteration, or whenever __getitem__ is invoked.

+
+ +
+
+metadata: list
+

A list containing the metadata for all individual time series in the dataset.

+
+ +
+
+describe()
+
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+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

Loading Custom Datasets

+

This notebook will explain how to load custom datasets saved to CSV files, for either anomaly detection or forecasting.

+
+

Anomaly Detection Datasets

+

Let’s first look at a synthetic anomaly detection dataset. Note that this section just provides an alternative implementation of the dataset ts_datasets.anomaly.Synthetic. We begin by listing all the CSV files in the relevant directory.

+
+
[1]:
+
+
+
import glob
+import os
+anom_dir = os.path.join("..", "data", "synthetic_anomaly")
+csvs = sorted(glob.glob(f"{anom_dir}/*.csv"))
+for csv in csvs:
+    print(csv)
+
+
+
+
+
+
+
+
+../data/synthetic_anomaly/horizontal.csv
+../data/synthetic_anomaly/horizontal_dip_anomaly.csv
+../data/synthetic_anomaly/horizontal_level_anomaly.csv
+../data/synthetic_anomaly/horizontal_shock_anomaly.csv
+../data/synthetic_anomaly/horizontal_spike_anomaly.csv
+../data/synthetic_anomaly/horizontal_trend_anomaly.csv
+../data/synthetic_anomaly/seasonal.csv
+../data/synthetic_anomaly/seasonal_dip_anomaly.csv
+../data/synthetic_anomaly/seasonal_level_anomaly.csv
+../data/synthetic_anomaly/seasonal_shock_anomaly.csv
+../data/synthetic_anomaly/seasonal_spike_anomaly.csv
+../data/synthetic_anomaly/seasonal_trend_anomaly.csv
+../data/synthetic_anomaly/upward_downward.csv
+../data/synthetic_anomaly/upward_downward_dip_anomaly.csv
+../data/synthetic_anomaly/upward_downward_level_anomaly.csv
+../data/synthetic_anomaly/upward_downward_shock_anomaly.csv
+../data/synthetic_anomaly/upward_downward_spike_anomaly.csv
+../data/synthetic_anomaly/upward_downward_trend_anomaly.csv
+
+
+

Let’s visualize what a couple of these CSVs look like.

+
+
[2]:
+
+
+
import pandas as pd
+from IPython.display import display
+
+for csv in [csvs[0], csvs[8]]:
+    print(csv)
+    display(pd.read_csv(csv))
+
+
+
+
+
+
+
+
+../data/synthetic_anomaly/horizontal.csv
+
+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
timestamphorizontal
001.928031
1300-1.156620
2600-0.390650
39000.400804
41200-0.874490
.........
999529985000.362724
999629988002.657373
999729991001.472341
999829994001.033154
999929997002.950466
+

10000 rows × 2 columns

+
+
+
+
+
+
+
+../data/synthetic_anomaly/seasonal_level_anomaly.csv
+
+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
timestampseasonalanomaly
00-0.5778830.0
13001.0597790.0
26001.1376090.0
39000.7433600.0
412001.9984000.0
............
99952998500-5.3886850.0
99962998800-5.0178280.0
99972999100-4.1967910.0
99982999400-4.2345550.0
99992999700-3.1116850.0
+

10000 rows × 3 columns

+
+
+

Each CSV in the dataset has the following important characteristics:

+
    +

  • a time column timestamp (here, a Unix timestamp expressed in units of seconds);

    • +

  • a column anomaly indicating whether a timestamp is anomalous or not (though this is absent for time series which don’t contain any anomalies);

    • +

  • one or more columns for the actual data values

    • +
+

We can create a data loader for all the CSV files in this dataset as follows:

+
+
[3]:
+
+
+
from ts_datasets.anomaly import CustomAnomalyDataset
+dataset = CustomAnomalyDataset(
+    rootdir=anom_dir,       # where the data is stored
+    test_frac=0.75,         # use 75% of each time series for testing.
+                            # overridden if the column `trainval` is in the actual CSV.
+    time_unit="s",          # the timestamp column (automatically detected) is in units of seconds
+    assume_no_anomaly=True  # if a CSV doesn't have the "anomaly" column, assume it has no anomalies
+)
+
+
+
+
+
[4]:
+
+
+
print(f"There are {len(dataset)} time series in this dataset.")
+time_series, metadata = dataset[3]
+
+
+
+
+
+
+
+
+There are 18 time series in this dataset.
+
+
+

This particular time series is univariate. Its variable is named “horizontal”.

+
+
[5]:
+
+
+
display(time_series)
+
+
+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
horizontal
timestamp
1970-01-01 00:00:001.928031
1970-01-01 00:05:00-1.156620
1970-01-01 00:10:00-0.390650
1970-01-01 00:15:000.400804
1970-01-01 00:20:00-0.874490
......
1970-02-04 16:55:000.362724
1970-02-04 17:00:002.657373
1970-02-04 17:05:001.472341
1970-02-04 17:10:001.033154
1970-02-04 17:15:002.950466
+

10000 rows × 1 columns

+
+
+

The metadata has the same timestamps as the time series. It contains “anomaly” and “trainval” columns. These respectively indicate whether each timestamp is anomalous, and whether each timestamp is for training/validation or testing.

+
+
[6]:
+
+
+
display(metadata)
+
+
+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
anomalytrainval
timestamp
1970-01-01 00:00:00FalseTrue
1970-01-01 00:05:00FalseTrue
1970-01-01 00:10:00FalseTrue
1970-01-01 00:15:00FalseTrue
1970-01-01 00:20:00FalseTrue
.........
1970-02-04 16:55:00FalseFalse
1970-02-04 17:00:00FalseFalse
1970-02-04 17:05:00FalseFalse
1970-02-04 17:10:00FalseFalse
1970-02-04 17:15:00FalseFalse
+

10000 rows × 2 columns

+
+
+
+
[7]:
+
+
+
print(f"{100 - metadata.trainval.mean() * 100}% of the time series is for testing.")
+print(f"{metadata.anomaly.mean() * 100}% of the time series is anomalous.")
+
+
+
+
+
+
+
+
+75.0% of the time series is for testing.
+19.57% of the time series is anomalous.
+
+
+
+
+

General Purpose (Forecasting) Datasets

+

Next, let’s load a more general-purpose dataset for forecasting. We will use this opportunity to show some of the more advanced features as well. Here, our dataset consists of a single CSV file which contains many multivariate time series. These time series are collected from a large retailer, and each individual time series corresonds to a different department within a different store. Let’s have a look at the data.

+
+
[8]:
+
+
+
csv = os.path.join("..", "data", "walmart", "walmart_mini.csv")
+display(pd.read_csv(csv))
+
+
+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
StoreDeptDateWeekly_SalesTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemploymentIsHoliday
0112010-02-0524924.5042.312.572NaNNaNNaNNaNNaN211.0963588.106False
1112010-02-1246039.4938.512.548NaNNaNNaNNaNNaN211.2421708.106True
2112010-02-1941595.5539.932.514NaNNaNNaNNaNNaN211.2891438.106False
3112010-02-2619403.5446.632.561NaNNaNNaNNaNNaN211.3196438.106False
4112010-03-0521827.9046.502.625NaNNaNNaNNaNNaN211.3501438.106False
.............................................
28552102012-09-2837104.6779.453.6667106.051.911.651549.103946.03222.6164336.565False
28562102012-10-0536361.2870.273.6176037.76NaN10.043027.373853.40222.8159306.170False
28572102012-10-1235332.3460.973.6012145.50NaN33.31586.8310421.01223.0154266.170False
28582102012-10-1935721.0968.083.5944461.89NaN1.141579.672642.29223.0598086.170False
28592102012-10-2634260.7669.793.5066152.59129.77200.00272.292924.15223.0783376.170False
+

2860 rows × 14 columns

+
+
+

As before, we have a column Date indicating the time. Note that in this case, we have a string rather than a timestamp; this is also okay. However, we now also have some index columns Store and Dept which are used to distinguish between different time series. We specify these to the data loader.

+
+
[9]:
+
+
+
from ts_datasets.forecast import CustomDataset
+dataset = CustomDataset(
+    rootdir=csv,                  # where the data is stored
+    index_cols=["Store", "Dept"], # Individual time series are indexed by store & department
+    test_frac=0.75,               # use 25% of each time series for testing.
+                                  # overridden if the column `trainval` is in the actual CSV.
+)
+
+
+
+
+
[10]:
+
+
+
print(f"There are {len(dataset)} time series in this dataset.")
+time_series, metadata = dataset[17]
+
+
+
+
+
+
+
+
+There are 20 time series in this dataset.
+
+
+

This particular time series is multivariate.

+
+
[11]:
+
+
+
display(time_series)
+
+
+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Weekly_SalesTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemploymentIsHoliday
Date
2010-02-0569634.8040.192.572NaNNaNNaNNaNNaN210.7526058.324False
2010-02-1263393.2938.492.548NaNNaNNaNNaNNaN210.8979948.324True
2010-02-1966589.2739.692.514NaNNaNNaNNaNNaN210.9451608.324False
2010-02-2661875.4846.102.561NaNNaNNaNNaNNaN210.9759578.324False
2010-03-0567041.1847.172.625NaNNaNNaNNaNNaN211.0067548.324False
....................................
2012-09-2857424.0079.453.6667106.051.911.651549.103946.03222.6164336.565False
2012-10-0562955.5170.273.6176037.76NaN10.043027.373853.40222.8159306.170False
2012-10-1263083.6360.973.6012145.50NaN33.31586.8310421.01223.0154266.170False
2012-10-1960502.9768.083.5944461.89NaN1.141579.672642.29223.0598086.170False
2012-10-2663992.3669.793.5066152.59129.77200.00272.292924.15223.0783376.170False
+

143 rows × 11 columns

+
+
+

The metadata has the same timestamps as the time series. It has a “trainval” column as before, plus index columns “Store” and “Dept”.

+
+
[12]:
+
+
+
display(metadata)
+
+
+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
trainvalStoreDept
Date
2010-02-05True28
2010-02-12True28
2010-02-19True28
2010-02-26True28
2010-03-05True28
............
2012-09-28False28
2012-10-05False28
2012-10-12False28
2012-10-19False28
2012-10-26False28
+

143 rows × 3 columns

+
+
+
+
+

Broader Takeaways

+

In general, a dataset can contain any number of CSVs stored under a single root directory. Each CSV can contain one or more time series, where the different time series within a single file are indicated by different values of the index column. Note that this works for anomaly detection as well! You just need to make sure that your CSVs all contain the anomaly column. In general, all features supported by CustomDataset are also supported by CustomAnomalyDataset, as long as your CSV +files have the anomaly column.

+

If you want to either of the above custom datasets for benchmarking, you can call

+
python benchmark_anomaly.py --model IsolationForest --retrain_freq 7d \
+    --dataset CustomAnomalyDataset --data_root data/synthetic_anomaly \
+    --data_kwargs '{"assume_no_anomaly": true, "test_frac": 0.75}'
+
+
+

or

+
python benchmark_forecast.py --model AutoETS  \
+    --dataset CustomDataset --data_root data/walmart/walmart_mini.csv \
+    --data_kwargs '{"test_frac": 0.25, \
+                    "index_cols": ["Store", "Dept"], \
+                    "data_cols": ["Weekly_Sales"]}'
+
+
+

Note in the example above, we specify “data_cols” as “Weekly_Sales”. This indicates that the only column we are modeling is Weekly_Sales. If you wanted to do multivariate prediction, you could also add “Temperature”, “Fuel_Price”, “CPI”, etc. We treat the first of the data columns as the target univariate whose value you wish to forecast.

+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/CustomDataset.ipynb b/v2.0.2/tutorials/CustomDataset.ipynb new file mode 100644 index 000000000..548990ea0 --- /dev/null +++ b/v2.0.2/tutorials/CustomDataset.ipynb @@ -0,0 +1,1457 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f32100be", + "metadata": {}, + "source": [ + "# Loading Custom Datasets\n", + "\n", + "This notebook will explain how to load custom datasets saved to CSV files, for either anomaly detection or forecasting." + ] + }, + { + "cell_type": "markdown", + "id": "91095c9b", + "metadata": {}, + "source": [ + "## Anomaly Detection Datasets\n", + "\n", + "Let's first look at a synthetic anomaly detection dataset. Note that this section just provides an alternative implementation of the dataset `ts_datasets.anomaly.Synthetic`. We begin by listing all the CSV files in the relevant directory. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b4886d69", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "../data/synthetic_anomaly/horizontal.csv\n", + "../data/synthetic_anomaly/horizontal_dip_anomaly.csv\n", + "../data/synthetic_anomaly/horizontal_level_anomaly.csv\n", + "../data/synthetic_anomaly/horizontal_shock_anomaly.csv\n", + "../data/synthetic_anomaly/horizontal_spike_anomaly.csv\n", + "../data/synthetic_anomaly/horizontal_trend_anomaly.csv\n", + "../data/synthetic_anomaly/seasonal.csv\n", + "../data/synthetic_anomaly/seasonal_dip_anomaly.csv\n", + "../data/synthetic_anomaly/seasonal_level_anomaly.csv\n", + "../data/synthetic_anomaly/seasonal_shock_anomaly.csv\n", + "../data/synthetic_anomaly/seasonal_spike_anomaly.csv\n", + "../data/synthetic_anomaly/seasonal_trend_anomaly.csv\n", + "../data/synthetic_anomaly/upward_downward.csv\n", + "../data/synthetic_anomaly/upward_downward_dip_anomaly.csv\n", + "../data/synthetic_anomaly/upward_downward_level_anomaly.csv\n", + "../data/synthetic_anomaly/upward_downward_shock_anomaly.csv\n", + "../data/synthetic_anomaly/upward_downward_spike_anomaly.csv\n", + "../data/synthetic_anomaly/upward_downward_trend_anomaly.csv\n" + ] + } + ], + "source": [ + "import glob\n", + "import os\n", + "anom_dir = os.path.join(\"..\", \"data\", \"synthetic_anomaly\")\n", + "csvs = sorted(glob.glob(f\"{anom_dir}/*.csv\"))\n", + "for csv in csvs:\n", + " print(csv)" + ] + }, + { + "cell_type": "markdown", + "id": "9d319673", + "metadata": {}, + "source": [ + "Let's visualize what a couple of these CSVs look like." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3151334c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "../data/synthetic_anomaly/horizontal.csv\n" + ] + }, + { + "data": { + "text/html": [ + "
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timestampseasonalanomaly
00-0.5778830.0
13001.0597790.0
26001.1376090.0
39000.7433600.0
412001.9984000.0
............
99952998500-5.3886850.0
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99972999100-4.1967910.0
99982999400-4.2345550.0
99992999700-3.1116850.0
\n", + "

10000 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " timestamp seasonal anomaly\n", + "0 0 -0.577883 0.0\n", + "1 300 1.059779 0.0\n", + "2 600 1.137609 0.0\n", + "3 900 0.743360 0.0\n", + "4 1200 1.998400 0.0\n", + "... ... ... ...\n", + "9995 2998500 -5.388685 0.0\n", + "9996 2998800 -5.017828 0.0\n", + "9997 2999100 -4.196791 0.0\n", + "9998 2999400 -4.234555 0.0\n", + "9999 2999700 -3.111685 0.0\n", + "\n", + "[10000 rows x 3 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "from IPython.display import display\n", + "\n", + "for csv in [csvs[0], csvs[8]]:\n", + " print(csv)\n", + " display(pd.read_csv(csv))" + ] + }, + { + "cell_type": "markdown", + "id": "4dd0360b", + "metadata": {}, + "source": [ + "Each CSV in the dataset has the following important characteristics:\n", + "\n", + "- a time column `timestamp` (here, a Unix timestamp expressed in units of seconds);\n", + "- a column `anomaly` indicating whether a timestamp is anomalous or not (though this is absent for time series which don't contain any anomalies);\n", + "- one or more columns for the actual data values\n", + "\n", + "We can create a data loader for all the CSV files in this dataset as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "69bbc96d", + "metadata": {}, + "outputs": [], + "source": [ + "from ts_datasets.anomaly import CustomAnomalyDataset\n", + "dataset = CustomAnomalyDataset(\n", + " rootdir=anom_dir, # where the data is stored\n", + " test_frac=0.75, # use 75% of each time series for testing. \n", + " # overridden if the column `trainval` is in the actual CSV.\n", + " time_unit=\"s\", # the timestamp column (automatically detected) is in units of seconds\n", + " assume_no_anomaly=True # if a CSV doesn't have the \"anomaly\" column, assume it has no anomalies\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bc2d0778", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 18 time series in this dataset.\n" + ] + } + ], + "source": [ + "print(f\"There are {len(dataset)} time series in this dataset.\")\n", + "time_series, metadata = dataset[3]" + ] + }, + { + "cell_type": "markdown", + "id": "9d1f1568", + "metadata": {}, + "source": [ + "This particular time series is univariate. Its variable is named \"horizontal\". " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c2a87bf9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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horizontal
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1970-01-01 00:00:001.928031
1970-01-01 00:05:00-1.156620
1970-01-01 00:10:00-0.390650
1970-01-01 00:15:000.400804
1970-01-01 00:20:00-0.874490
......
1970-02-04 16:55:000.362724
1970-02-04 17:00:002.657373
1970-02-04 17:05:001.472341
1970-02-04 17:10:001.033154
1970-02-04 17:15:002.950466
\n", + "

10000 rows × 1 columns

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" + ], + "text/plain": [ + " horizontal\n", + "timestamp \n", + "1970-01-01 00:00:00 1.928031\n", + "1970-01-01 00:05:00 -1.156620\n", + "1970-01-01 00:10:00 -0.390650\n", + "1970-01-01 00:15:00 0.400804\n", + "1970-01-01 00:20:00 -0.874490\n", + "... ...\n", + "1970-02-04 16:55:00 0.362724\n", + "1970-02-04 17:00:00 2.657373\n", + "1970-02-04 17:05:00 1.472341\n", + "1970-02-04 17:10:00 1.033154\n", + "1970-02-04 17:15:00 2.950466\n", + "\n", + "[10000 rows x 1 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(time_series)" + ] + }, + { + "cell_type": "markdown", + "id": "ec03b3f1", + "metadata": {}, + "source": [ + "The metadata has the same timestamps as the time series. It contains \"anomaly\" and \"trainval\" columns. These respectively indicate whether each timestamp is anomalous, and whether each timestamp is for training/validation or testing." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3e5eb1d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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anomalytrainval
timestamp
1970-01-01 00:00:00FalseTrue
1970-01-01 00:05:00FalseTrue
1970-01-01 00:10:00FalseTrue
1970-01-01 00:15:00FalseTrue
1970-01-01 00:20:00FalseTrue
.........
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10000 rows × 2 columns

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" + ], + "text/plain": [ + " anomaly trainval\n", + "timestamp \n", + "1970-01-01 00:00:00 False True\n", + "1970-01-01 00:05:00 False True\n", + "1970-01-01 00:10:00 False True\n", + "1970-01-01 00:15:00 False True\n", + "1970-01-01 00:20:00 False True\n", + "... ... ...\n", + "1970-02-04 16:55:00 False False\n", + "1970-02-04 17:00:00 False False\n", + "1970-02-04 17:05:00 False False\n", + "1970-02-04 17:10:00 False False\n", + "1970-02-04 17:15:00 False False\n", + "\n", + "[10000 rows x 2 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(metadata)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a911fea8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "75.0% of the time series is for testing.\n", + "19.57% of the time series is anomalous.\n" + ] + } + ], + "source": [ + "print(f\"{100 - metadata.trainval.mean() * 100}% of the time series is for testing.\")\n", + "print(f\"{metadata.anomaly.mean() * 100}% of the time series is anomalous.\")" + ] + }, + { + "cell_type": "markdown", + "id": "63a181a3", + "metadata": {}, + "source": [ + "## General Purpose (Forecasting) Datasets\n", + "\n", + "Next, let's load a more general-purpose dataset for forecasting. We will use this opportunity to show some of the more advanced features as well. Here, our dataset consists of a single CSV file which contains many multivariate time series. These time series are collected from a large retailer, and each individual time series corresonds to a different department within a different store. Let's have a look at the data." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2d0809ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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StoreDeptDateWeekly_SalesTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemploymentIsHoliday
0112010-02-0524924.5042.312.572NaNNaNNaNNaNNaN211.0963588.106False
1112010-02-1246039.4938.512.548NaNNaNNaNNaNNaN211.2421708.106True
2112010-02-1941595.5539.932.514NaNNaNNaNNaNNaN211.2891438.106False
3112010-02-2619403.5446.632.561NaNNaNNaNNaNNaN211.3196438.106False
4112010-03-0521827.9046.502.625NaNNaNNaNNaNNaN211.3501438.106False
.............................................
28552102012-09-2837104.6779.453.6667106.051.911.651549.103946.03222.6164336.565False
28562102012-10-0536361.2870.273.6176037.76NaN10.043027.373853.40222.8159306.170False
28572102012-10-1235332.3460.973.6012145.50NaN33.31586.8310421.01223.0154266.170False
28582102012-10-1935721.0968.083.5944461.89NaN1.141579.672642.29223.0598086.170False
28592102012-10-2634260.7669.793.5066152.59129.77200.00272.292924.15223.0783376.170False
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2860 rows × 14 columns

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" + ], + "text/plain": [ + " Store Dept Date Weekly_Sales Temperature Fuel_Price \\\n", + "0 1 1 2010-02-05 24924.50 42.31 2.572 \n", + "1 1 1 2010-02-12 46039.49 38.51 2.548 \n", + "2 1 1 2010-02-19 41595.55 39.93 2.514 \n", + "3 1 1 2010-02-26 19403.54 46.63 2.561 \n", + "4 1 1 2010-03-05 21827.90 46.50 2.625 \n", + "... ... ... ... ... ... ... \n", + "2855 2 10 2012-09-28 37104.67 79.45 3.666 \n", + "2856 2 10 2012-10-05 36361.28 70.27 3.617 \n", + "2857 2 10 2012-10-12 35332.34 60.97 3.601 \n", + "2858 2 10 2012-10-19 35721.09 68.08 3.594 \n", + "2859 2 10 2012-10-26 34260.76 69.79 3.506 \n", + "\n", + " MarkDown1 MarkDown2 MarkDown3 MarkDown4 MarkDown5 CPI \\\n", + "0 NaN NaN NaN NaN NaN 211.096358 \n", + "1 NaN NaN NaN NaN NaN 211.242170 \n", + "2 NaN NaN NaN NaN NaN 211.289143 \n", + "3 NaN NaN NaN NaN NaN 211.319643 \n", + "4 NaN NaN NaN NaN NaN 211.350143 \n", + "... ... ... ... ... ... ... \n", + "2855 7106.05 1.91 1.65 1549.10 3946.03 222.616433 \n", + "2856 6037.76 NaN 10.04 3027.37 3853.40 222.815930 \n", + "2857 2145.50 NaN 33.31 586.83 10421.01 223.015426 \n", + "2858 4461.89 NaN 1.14 1579.67 2642.29 223.059808 \n", + "2859 6152.59 129.77 200.00 272.29 2924.15 223.078337 \n", + "\n", + " Unemployment IsHoliday \n", + "0 8.106 False \n", + "1 8.106 True \n", + "2 8.106 False \n", + "3 8.106 False \n", + "4 8.106 False \n", + "... ... ... \n", + "2855 6.565 False \n", + "2856 6.170 False \n", + "2857 6.170 False \n", + "2858 6.170 False \n", + "2859 6.170 False \n", + "\n", + "[2860 rows x 14 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "csv = os.path.join(\"..\", \"data\", \"walmart\", \"walmart_mini.csv\")\n", + "display(pd.read_csv(csv))" + ] + }, + { + "cell_type": "markdown", + "id": "5fde813d", + "metadata": {}, + "source": [ + "As before, we have a column `Date` indicating the time. Note that in this case, we have a string rather than a timestamp; this is also okay. However, we now also have some index columns `Store` and `Dept` which are used to distinguish between different time series. We specify these to the data loader." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fe500896", + "metadata": {}, + "outputs": [], + "source": [ + "from ts_datasets.forecast import CustomDataset\n", + "dataset = CustomDataset(\n", + " rootdir=csv, # where the data is stored\n", + " index_cols=[\"Store\", \"Dept\"], # Individual time series are indexed by store & department\n", + " test_frac=0.75, # use 25% of each time series for testing. \n", + " # overridden if the column `trainval` is in the actual CSV.\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8ca5296f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 20 time series in this dataset.\n" + ] + } + ], + "source": [ + "print(f\"There are {len(dataset)} time series in this dataset.\")\n", + "time_series, metadata = dataset[17]" + ] + }, + { + "cell_type": "markdown", + "id": "7cfc92a8", + "metadata": {}, + "source": [ + "This particular time series is multivariate." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "301d9344", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Weekly_SalesTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemploymentIsHoliday
Date
2010-02-0569634.8040.192.572NaNNaNNaNNaNNaN210.7526058.324False
2010-02-1263393.2938.492.548NaNNaNNaNNaNNaN210.8979948.324True
2010-02-1966589.2739.692.514NaNNaNNaNNaNNaN210.9451608.324False
2010-02-2661875.4846.102.561NaNNaNNaNNaNNaN210.9759578.324False
2010-03-0567041.1847.172.625NaNNaNNaNNaNNaN211.0067548.324False
....................................
2012-09-2857424.0079.453.6667106.051.911.651549.103946.03222.6164336.565False
2012-10-0562955.5170.273.6176037.76NaN10.043027.373853.40222.8159306.170False
2012-10-1263083.6360.973.6012145.50NaN33.31586.8310421.01223.0154266.170False
2012-10-1960502.9768.083.5944461.89NaN1.141579.672642.29223.0598086.170False
2012-10-2663992.3669.793.5066152.59129.77200.00272.292924.15223.0783376.170False
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143 rows × 11 columns

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" + ], + "text/plain": [ + " Weekly_Sales Temperature Fuel_Price MarkDown1 MarkDown2 \\\n", + "Date \n", + "2010-02-05 69634.80 40.19 2.572 NaN NaN \n", + "2010-02-12 63393.29 38.49 2.548 NaN NaN \n", + "2010-02-19 66589.27 39.69 2.514 NaN NaN \n", + "2010-02-26 61875.48 46.10 2.561 NaN NaN \n", + "2010-03-05 67041.18 47.17 2.625 NaN NaN \n", + "... ... ... ... ... ... \n", + "2012-09-28 57424.00 79.45 3.666 7106.05 1.91 \n", + "2012-10-05 62955.51 70.27 3.617 6037.76 NaN \n", + "2012-10-12 63083.63 60.97 3.601 2145.50 NaN \n", + "2012-10-19 60502.97 68.08 3.594 4461.89 NaN \n", + "2012-10-26 63992.36 69.79 3.506 6152.59 129.77 \n", + "\n", + " MarkDown3 MarkDown4 MarkDown5 CPI Unemployment \\\n", + "Date \n", + "2010-02-05 NaN NaN NaN 210.752605 8.324 \n", + "2010-02-12 NaN NaN NaN 210.897994 8.324 \n", + "2010-02-19 NaN NaN NaN 210.945160 8.324 \n", + "2010-02-26 NaN NaN NaN 210.975957 8.324 \n", + "2010-03-05 NaN NaN NaN 211.006754 8.324 \n", + "... ... ... ... ... ... \n", + "2012-09-28 1.65 1549.10 3946.03 222.616433 6.565 \n", + "2012-10-05 10.04 3027.37 3853.40 222.815930 6.170 \n", + "2012-10-12 33.31 586.83 10421.01 223.015426 6.170 \n", + "2012-10-19 1.14 1579.67 2642.29 223.059808 6.170 \n", + "2012-10-26 200.00 272.29 2924.15 223.078337 6.170 \n", + "\n", + " IsHoliday \n", + "Date \n", + "2010-02-05 False \n", + "2010-02-12 True \n", + "2010-02-19 False \n", + "2010-02-26 False \n", + "2010-03-05 False \n", + "... ... \n", + "2012-09-28 False \n", + "2012-10-05 False \n", + "2012-10-12 False \n", + "2012-10-19 False \n", + "2012-10-26 False \n", + "\n", + "[143 rows x 11 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(time_series)" + ] + }, + { + "cell_type": "markdown", + "id": "33926c81", + "metadata": {}, + "source": [ + "The metadata has the same timestamps as the time series. It has a \"trainval\" column as before, plus index columns \"Store\" and \"Dept\"." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4d3cd301", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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trainvalStoreDept
Date
2010-02-05True28
2010-02-12True28
2010-02-19True28
2010-02-26True28
2010-03-05True28
............
2012-09-28False28
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\n", + "

143 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " trainval Store Dept\n", + "Date \n", + "2010-02-05 True 2 8\n", + "2010-02-12 True 2 8\n", + "2010-02-19 True 2 8\n", + "2010-02-26 True 2 8\n", + "2010-03-05 True 2 8\n", + "... ... ... ...\n", + "2012-09-28 False 2 8\n", + "2012-10-05 False 2 8\n", + "2012-10-12 False 2 8\n", + "2012-10-19 False 2 8\n", + "2012-10-26 False 2 8\n", + "\n", + "[143 rows x 3 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(metadata)" + ] + }, + { + "cell_type": "markdown", + "id": "19562928", + "metadata": {}, + "source": [ + "## Broader Takeaways\n", + "\n", + "In general, a dataset can contain any number of CSVs stored under a single root directory. Each CSV can contain one or more time series, where the different time series within a single file are indicated by different values of the index column. Note that this works for anomaly detection as well! You just need to make sure that your CSVs all contain the `anomaly` column. In general, all features supported by `CustomDataset` are also supported by `CustomAnomalyDataset`, as long as your CSV files have the `anomaly` column.\n", + "\n", + "If you want to either of the above custom datasets for benchmarking, you can call\n", + "\n", + "```\n", + "python benchmark_anomaly.py --model IsolationForest --retrain_freq 7d \\\n", + " --dataset CustomAnomalyDataset --data_root data/synthetic_anomaly \\\n", + " --data_kwargs '{\"assume_no_anomaly\": true, \"test_frac\": 0.75}'\n", + "```\n", + "\n", + "or \n", + "\n", + "```\n", + "python benchmark_forecast.py --model AutoETS \\\n", + " --dataset CustomDataset --data_root data/walmart/walmart_mini.csv \\\n", + " --data_kwargs '{\"test_frac\": 0.25, \\\n", + " \"index_cols\": [\"Store\", \"Dept\"], \\\n", + " \"data_cols\": [\"Weekly_Sales\"]}'\n", + "```\n", + "\n", + "Note in the example above, we specify \"data_cols\" as \"Weekly_Sales\". This indicates that the only column we are modeling is Weekly_Sales. If you wanted to do multivariate prediction, you could also add \"Temperature\", \"Fuel_Price\", \"CPI\", etc. We treat the first of the data columns as the target univariate whose value you wish to forecast." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/v2.0.2/tutorials/TimeSeries.html b/v2.0.2/tutorials/TimeSeries.html new file mode 100644 index 000000000..4d2bb65c3 --- /dev/null +++ b/v2.0.2/tutorials/TimeSeries.html @@ -0,0 +1,1329 @@ + + + + + + Merlion’s Data Format — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

Merlion’s Data Format

+

This notebook will explain how to use Merlion’s UnivariateTimeSeries and TimeSeries classes. These classes are the core data format used throughout the repo. In general, you may think of each TimeSeries as being a collection of UnivariateTimeSeries objects, one for each variable.

+

Let’s start by loading some data using pandas.

+
+
[1]:
+
+
+
import os
+import pandas as pd
+
+df = pd.read_csv(os.path.join("..", "data", "example.csv"))
+print(df)
+
+
+
+
+
+
+
+
+       timestamp_millis       kpi  kpi_label
+0         1583140320000   667.118          0
+1         1583140380000   611.751          0
+2         1583140440000   599.456          0
+3         1583140500000   621.446          0
+4         1583140560000  1418.234          0
+...                 ...       ...        ...
+86802     1588376760000   874.214          0
+86803     1588376820000   937.929          0
+86804     1588376880000  1031.279          0
+86805     1588376940000  1099.698          0
+86806     1588377000000   935.405          0
+
+[86807 rows x 3 columns]
+
+
+

The column timestamp_millis consists of Unix timestamps (in units of milliseconds), and the column kpi contains the value of the time series metric at each of those timestamps. We will also create a version of this dataframe that is indexed by time:

+
+
[2]:
+
+
+
time_idx_df = df.copy()
+time_idx_df["timestamp_millis"] = pd.to_datetime(time_idx_df["timestamp_millis"], unit="ms")
+time_idx_df = time_idx_df.set_index("timestamp_millis")
+print(time_idx_df)
+
+
+
+
+
+
+
+
+                          kpi  kpi_label
+timestamp_millis
+2020-03-02 09:12:00   667.118          0
+2020-03-02 09:13:00   611.751          0
+2020-03-02 09:14:00   599.456          0
+2020-03-02 09:15:00   621.446          0
+2020-03-02 09:16:00  1418.234          0
+...                       ...        ...
+2020-05-01 23:46:00   874.214          0
+2020-05-01 23:47:00   937.929          0
+2020-05-01 23:48:00  1031.279          0
+2020-05-01 23:49:00  1099.698          0
+2020-05-01 23:50:00   935.405          0
+
+[86807 rows x 2 columns]
+
+
+
+

UnivariateTimeSeries: The Basic Building Block

+

The most transparent way to initialize a UnivariateTimeSeries is to use its constructor. The constructor takes two arguments: time_stamps, a list of Unix timestamps (in units of seconds) or datetime objects, and values, a list of the actual time series values. You may optionally provide a name as well.

+
+
[3]:
+
+
+
from merlion.utils import UnivariateTimeSeries
+
+kpi = UnivariateTimeSeries(
+    time_stamps=df.timestamp_millis/1000,  # timestamps in units of seconds
+    values=df.kpi,                         # time series values
+    name="kpi"                             # optional: a name for this univariate
+)
+
+kpi_label = UnivariateTimeSeries(
+    time_stamps=df.timestamp_millis/1000,  # timestamps in units of seconds
+    values=df.kpi_label                    # time series values
+)
+
+
+
+

Alternatively, you may initialize a UnivariateTimeSeries directly from a time-indexed pd.Series:

+
+
[4]:
+
+
+
kpi_equivalent = UnivariateTimeSeries.from_pd(time_idx_df.kpi)
+print(f"Are the two UnivariateTimeSeries equal? {(kpi == kpi_equivalent).all()}")
+
+
+
+
+
+
+
+
+Are the two UnivariateTimeSeries equal? True
+
+
+

We implement the UnivariateTimeSeries as a pd.Series with a DatetimeIndex:

+
+
[5]:
+
+
+
print(f"Is {type(kpi).__name__} an instance of pd.Series? "
+      f"{isinstance(kpi, pd.Series)}")
+
+
+
+
+
+
+
+
+Is UnivariateTimeSeries an instance of pd.Series? True
+
+
+
+
[6]:
+
+
+
print(kpi)
+
+
+
+
+
+
+
+
+time
+2020-03-02 09:12:00     667.118
+2020-03-02 09:13:00     611.751
+2020-03-02 09:14:00     599.456
+2020-03-02 09:15:00     621.446
+2020-03-02 09:16:00    1418.234
+                         ...
+2020-05-01 23:46:00     874.214
+2020-05-01 23:47:00     937.929
+2020-05-01 23:48:00    1031.279
+2020-05-01 23:49:00    1099.698
+2020-05-01 23:50:00     935.405
+Name: kpi, Length: 86807, dtype: float64
+
+
+

You can also convert a UnivariateTimeSeries back to a regular pd.Series as follows:

+
+
[7]:
+
+
+
print(f"type(kpi.to_pd()) = {type(kpi.to_pd())}")
+
+
+
+
+
+
+
+
+type(kpi.to_pd()) = <class 'pandas.core.series.Series'>
+
+
+

You can access the timestamps (either as timestamps or datetime objects) and values independently:

+
+
[8]:
+
+
+
# Get the Unix timestamps (first 5 for brevity)
+print(kpi.time_stamps[:5])
+
+
+
+
+
+
+
+
+[1583140320.0, 1583140380.0, 1583140440.0, 1583140500.0, 1583140560.0]
+
+
+
+
[9]:
+
+
+
# Get the datetimes (this is just the index of the UnivariateTimeSeries,
+# since we inherit from pd.Series)
+print(kpi.index[:5])
+
+
+
+
+
+
+
+
+DatetimeIndex(['2020-03-02 09:12:00', '2020-03-02 09:13:00',
+               '2020-03-02 09:14:00', '2020-03-02 09:15:00',
+               '2020-03-02 09:16:00'],
+              dtype='datetime64[ns]', name='time', freq=None)
+
+
+
+
[10]:
+
+
+
# Get the values
+print(kpi.values[:5])
+
+
+
+
+
+
+
+
+[667.118, 611.751, 599.456, 621.446, 1418.234]
+
+
+

You may index into a UnivariateTimeSeries to obtain a tuple of (timestamp, value):

+
+
[11]:
+
+
+
print(f"kpi[0] = {kpi[0]}")
+
+
+
+
+
+
+
+
+kpi[0] = (1583140320.0, 667.118)
+
+
+

If you instead use a slice index, you will obtain a new UnivariateTimeSeries:

+
+
[12]:
+
+
+
print(f"type(kpi[1:5]) = {type(kpi[1:5])}\n")
+print(f"kpi[1:5] = \n\n{kpi[1:5]}")
+
+
+
+
+
+
+
+
+type(kpi[1:5]) = <class 'merlion.utils.time_series.UnivariateTimeSeries'>
+
+kpi[1:5] =
+
+time
+2020-03-02 09:13:00     611.751
+2020-03-02 09:14:00     599.456
+2020-03-02 09:15:00     621.446
+2020-03-02 09:16:00    1418.234
+Name: kpi, dtype: float64
+
+
+

Iterating over a UnivaraiateTimeSeries will iterate over tuples of (timestamp, value):

+
+
[13]:
+
+
+
for t, x in kpi[:5]:
+    print((t, x))
+
+
+
+
+
+
+
+
+(1583140320.0, 667.118)
+(1583140380.0, 611.751)
+(1583140440.0, 599.456)
+(1583140500.0, 621.446)
+(1583140560.0, 1418.234)
+
+
+
+
+

TimeSeries: Merlion’s Standard Data Class

+

Because Merlion is a general-purpose library that handles both univariate and multivariate time series, our standard data class is TimeSeries. This class acts as a wrapper around a collection of UnivariateTimeSeries. We choose this format rather than a vector-based approach because this approach is much more robust to missing values, or different univariates being sampled at different rates.

+

The most transparent way to initialize a TimeSeries is with its constructor, which takes a collection (list or (ordered) dictionary) of UnivariateTimeSeries its only argument:

+
+
[14]:
+
+
+
from collections import OrderedDict
+from merlion.utils import TimeSeries
+
+time_series_list = TimeSeries(univariates=[kpi.copy(), kpi_label.copy()])
+time_series_dict = TimeSeries(
+    univariates=OrderedDict([("kpi_renamed", kpi.copy()),
+                             ("kpi_label", kpi_label.copy())]))
+
+
+
+

Alternatively, you may initialize a TimeSeries from a pd.DataFrame and convert a TimeSeries to a pd.DataFrame as follows:

+
+
[15]:
+
+
+
time_series = TimeSeries.from_pd(time_idx_df)
+print(f"type(TimeSeries.from_pd(time_idx_df)) = {type(time_series)}\n")
+
+recovered_time_idx_df = time_series.to_pd()
+print("(recovered_time_idx_df == time_idx_df).all()")
+print((recovered_time_idx_df == time_idx_df).all())
+
+
+
+
+
+
+
+
+type(TimeSeries.from_pd(time_idx_df)) = <class 'merlion.utils.time_series.TimeSeries'>
+
+(recovered_time_idx_df == time_idx_df).all()
+kpi          True
+kpi_label    True
+dtype: bool
+
+
+

We may access the names of the individual univariates with time_series.names, access a specific univariate via time_series.univariates[name], and iterate over univariates by iterating for univariate in time_series.univariates. Concretely:

+
+
[16]:
+
+
+
# When we use a list of univariates, we retain the names of the univariates
+# where possible. If a univariate is unnamed, we set its name to its integer
+# index in the list of all univariates given. Here, kpi_label was
+# originally unnamed, so we set its name to 1
+print(time_series_list.names)
+
+
+
+
+
+
+
+
+['kpi', 'kpi_label']
+
+
+
+
[17]:
+
+
+
# If we pass a dictionary instead of a list, all univariates will have
+# their specified names. The order is retained from the OrderedDict.
+print(time_series_dict.names)
+
+
+
+
+
+
+
+
+['kpi_renamed', 'kpi_label']
+
+
+
+
[18]:
+
+
+
# We can access the KPI like so:
+kpi1 = time_series_list.univariates["kpi"]
+kpi2 = time_series_dict.univariates["kpi_renamed"]
+
+# kpi1 and kpi2 are the same univariate, just with different names
+assert (kpi1 == kpi2).all()
+
+
+
+
+
[19]:
+
+
+
# We can iterate over all univariates like so:
+for univariate in time_series_dict.univariates:
+    print(univariate)
+    print()
+
+
+
+
+
+
+
+
+time
+2020-03-02 09:12:00     667.118
+2020-03-02 09:13:00     611.751
+2020-03-02 09:14:00     599.456
+2020-03-02 09:15:00     621.446
+2020-03-02 09:16:00    1418.234
+                         ...
+2020-05-01 23:46:00     874.214
+2020-05-01 23:47:00     937.929
+2020-05-01 23:48:00    1031.279
+2020-05-01 23:49:00    1099.698
+2020-05-01 23:50:00     935.405
+Name: kpi_renamed, Length: 86807, dtype: float64
+
+time
+2020-03-02 09:12:00    0.0
+2020-03-02 09:13:00    0.0
+2020-03-02 09:14:00    0.0
+2020-03-02 09:15:00    0.0
+2020-03-02 09:16:00    0.0
+                      ...
+2020-05-01 23:46:00    0.0
+2020-05-01 23:47:00    0.0
+2020-05-01 23:48:00    0.0
+2020-05-01 23:49:00    0.0
+2020-05-01 23:50:00    0.0
+Name: kpi_label, Length: 86807, dtype: float64
+
+
+
+
+
[20]:
+
+
+
# We can also iterate over all univariates & names like so:
+for name, univariate in time_series_dict.items():
+    print(f"Univariate {name}")
+    print(univariate)
+    print()
+
+
+
+
+
+
+
+
+Univariate kpi_renamed
+time
+2020-03-02 09:12:00     667.118
+2020-03-02 09:13:00     611.751
+2020-03-02 09:14:00     599.456
+2020-03-02 09:15:00     621.446
+2020-03-02 09:16:00    1418.234
+                         ...
+2020-05-01 23:46:00     874.214
+2020-05-01 23:47:00     937.929
+2020-05-01 23:48:00    1031.279
+2020-05-01 23:49:00    1099.698
+2020-05-01 23:50:00     935.405
+Name: kpi_renamed, Length: 86807, dtype: float64
+
+Univariate kpi_label
+time
+2020-03-02 09:12:00    0.0
+2020-03-02 09:13:00    0.0
+2020-03-02 09:14:00    0.0
+2020-03-02 09:15:00    0.0
+2020-03-02 09:16:00    0.0
+                      ...
+2020-05-01 23:46:00    0.0
+2020-05-01 23:47:00    0.0
+2020-05-01 23:48:00    0.0
+2020-05-01 23:49:00    0.0
+2020-05-01 23:50:00    0.0
+Name: kpi_label, Length: 86807, dtype: float64
+
+
+
+
+
+

Time Series Indexing & Alignment

+

An important concept of TimeSeries in Merlion is alignment. We call a time series aligned if all of its univariates are sampled at the same time stamps. We illustrate examples of time series that are and aren’t aligned below:

+
+
[21]:
+
+
+
aligned = TimeSeries({"kpi": kpi.copy(), "kpi_label": kpi_label.copy()})
+print(f"Is aligned? {aligned.is_aligned}")
+
+
+
+
+
+
+
+
+Is aligned? True
+
+
+
+
[22]:
+
+
+
not_aligned = TimeSeries({"kpi": kpi[1:],                # 2020-03-02 09:13:00 to 2020-05-01 23:50:00
+                          "kpi_label": kpi_label[:-1]})  # 2020-03-02 09:12:00 to 2020-05-01 23:49:00
+print(f"Is aligned? {not_aligned.is_aligned}")
+
+
+
+
+
+
+
+
+Is aligned? False
+
+
+

If your time series is aligned, you may use an integer index to obtain a tuple (timestamp, (value_1, ..., value_k)), or a slice index to obtain a sub-TimeSeries:

+
+
[23]:
+
+
+
aligned[0]
+
+
+
+
+
[23]:
+
+
+
+
+(1583140320.0, [667.118, 0.0])
+
+
+
+
[24]:
+
+
+
print(f"type(aligned[1:5]) = {type(aligned[1:5])}\n")
+print(f"aligned[1:5] = \n{aligned[1:5]}")
+
+
+
+
+
+
+
+
+type(aligned[1:5]) = <class 'merlion.utils.time_series.TimeSeries'>
+
+aligned[1:5] =
+                          kpi  kpi_label
+time
+2020-03-02 09:13:00   611.751        0.0
+2020-03-02 09:14:00   599.456        0.0
+2020-03-02 09:15:00   621.446        0.0
+2020-03-02 09:16:00  1418.234        0.0
+
+
+

You may also iterate over an aligned time series as for timestamp, (value_1, ..., value_k) in time_series:

+
+
[25]:
+
+
+
for t, (x1, x2) in aligned[:5]:
+    print((t, (x1, x2)))
+
+
+
+
+
+
+
+
+(1583140320.0, (667.118, 0.0))
+(1583140380.0, (611.751, 0.0))
+(1583140440.0, (599.456, 0.0))
+(1583140500.0, (621.446, 0.0))
+(1583140560.0, (1418.234, 0.0))
+
+
+

Note that Merlion will throw an error if you try to do any of these things with a time series that isn’t aligned! For example,

+
+
[26]:
+
+
+
try:
+    not_aligned[0]
+except RuntimeError as e:
+    print(f"{type(e).__name__}: {e}")
+
+
+
+
+
+
+
+
+RuntimeError: The univariates comprising this time series are not aligned (they have different time stamps), but alignment is required to index into the time series.
+
+
+

You can still get the length/shape of a misaligned time series, but Merlion will emit a warning.

+
+
[27]:
+
+
+
print(len(not_aligned))
+
+
+
+
+
+
+
+
+/Users/abhatnagar/Desktop/Merlion/merlion/utils/time_series.py:672: UserWarning: The univariates comprising this time series are not aligned (they have different time stamps). The length returned is equal to the length of the _union_ of all time stamps present in any of the univariates.
+  warnings.warn(warning)
+The univariates comprising this time series are not aligned (they have different time stamps). The length returned is equal to the length of the _union_ of all time stamps present in any of the univariates.
+
+
+
+
+
+
+
+86807
+
+
+
+
[28]:
+
+
+
print(not_aligned.shape)
+
+
+
+
+
+
+
+
+The univariates comprising this time series are not aligned (they have different time stamps). The length returned is equal to the length of the _union_ of all time stamps present in any of the univariates.
+
+
+
+
+
+
+
+(2, 86807)
+
+
+

However, you may call time_series.align() to automatically resample the individual univariates of a time series to make it aligned. By default, this will take the union of all the time stamps present in any of the individual univariates, but this is customizable.

+
+
[29]:
+
+
+
print(f"Is not_aligned.align() aligned? {not_aligned.align().is_aligned}")
+
+
+
+
+
+
+
+
+Is not_aligned.align() aligned? True
+
+
+
+
+

TimeSeries: A Few Useful Features

+

We provide much more information on the merlion.utils.time_series.TimeSeries class in the API docs, but we highlight two more useful features here. These work regardless of whether a time series is aligned!

+

You may obtain the subset of a time series between times t0 and tf by calling time_series.window(t0, tf). t0 and tf may be any reasonable format of datetime, or a Unix timestamp.

+
+
[30]:
+
+
+
aligned.window("2020-03-05 12:00:00", pd.Timestamp(year=2020, month=4, day=1))
+
+
+
+
+
[30]:
+
+
+
+
+                          kpi  kpi_label
+time
+2020-03-05 12:00:00  1166.819        0.0
+2020-03-05 12:01:00  1345.504        0.0
+2020-03-05 12:02:00  1061.391        0.0
+2020-03-05 12:03:00  1260.874        0.0
+2020-03-05 12:04:00  1202.009        0.0
+...                       ...        ...
+2020-03-31 23:55:00  1154.397        0.0
+2020-03-31 23:56:00  1270.292        0.0
+2020-03-31 23:57:00  1160.761        0.0
+2020-03-31 23:58:00  1082.076        0.0
+2020-03-31 23:59:00  1167.297        0.0
+
+[38160 rows x 2 columns]
+
+
+
+
[31]:
+
+
+
# Note that the first value of the KPI (which is missing in not_aligned) is NaN
+not_aligned.window(1583140320, 1583226720)
+
+
+
+
+
[31]:
+
+
+
+
+                          kpi  kpi_label
+time
+2020-03-02 09:12:00       NaN        0.0
+2020-03-02 09:13:00   611.751        0.0
+2020-03-02 09:14:00   599.456        0.0
+2020-03-02 09:15:00   621.446        0.0
+2020-03-02 09:16:00  1418.234        0.0
+...                       ...        ...
+2020-03-03 09:07:00  1132.564        0.0
+2020-03-03 09:08:00  1087.037        0.0
+2020-03-03 09:09:00   984.432        0.0
+2020-03-03 09:10:00  1085.008        0.0
+2020-03-03 09:11:00  1020.937        0.0
+
+[1440 rows x 2 columns]
+
+
+

You may also bisect a time series into a left and right portion, at any timestamp.

+
+
[32]:
+
+
+
left, right = aligned.bisect("2020-05-01")
+print(f"Left\n{left}\n")
+print()
+print(f"Right\n{right}\n")
+
+
+
+
+
+
+
+
+Left
+                          kpi  kpi_label
+time
+2020-03-02 09:12:00   667.118        0.0
+2020-03-02 09:13:00   611.751        0.0
+2020-03-02 09:14:00   599.456        0.0
+2020-03-02 09:15:00   621.446        0.0
+2020-03-02 09:16:00  1418.234        0.0
+...                       ...        ...
+2020-04-30 23:55:00  1296.091        0.0
+2020-04-30 23:56:00  1323.743        0.0
+2020-04-30 23:57:00  1203.672        0.0
+2020-04-30 23:58:00  1278.720        0.0
+2020-04-30 23:59:00  1217.877        0.0
+
+[85376 rows x 2 columns]
+
+
+Right
+                          kpi  kpi_label
+time
+2020-05-01 00:00:00  1381.110        0.0
+2020-05-01 00:01:00  1807.039        0.0
+2020-05-01 00:02:00  1833.385        0.0
+2020-05-01 00:03:00  1674.412        0.0
+2020-05-01 00:04:00  1683.194        0.0
+...                       ...        ...
+2020-05-01 23:46:00   874.214        0.0
+2020-05-01 23:47:00   937.929        0.0
+2020-05-01 23:48:00  1031.279        0.0
+2020-05-01 23:49:00  1099.698        0.0
+2020-05-01 23:50:00   935.405        0.0
+
+[1431 rows x 2 columns]
+
+
+
+

Please refer to the API docs on UnivariateTimeSeries and TimeSeries for more information.

+
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+
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+
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/TimeSeries.ipynb b/v2.0.2/tutorials/TimeSeries.ipynb new file mode 100644 index 000000000..d94aa95fd --- /dev/null +++ b/v2.0.2/tutorials/TimeSeries.ipynb @@ -0,0 +1,1002 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Merlion's Data Format\n", + "\n", + "This notebook will explain how to use Merlion's `UnivariateTimeSeries` and `TimeSeries` classes. These classes are the core data format used throughout the repo. In general, you may think of each `TimeSeries` as being a collection of `UnivariateTimeSeries` objects, one for each variable. \n", + "\n", + "Let's start by loading some data using `pandas`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " timestamp_millis kpi kpi_label\n", + "0 1583140320000 667.118 0\n", + "1 1583140380000 611.751 0\n", + "2 1583140440000 599.456 0\n", + "3 1583140500000 621.446 0\n", + "4 1583140560000 1418.234 0\n", + "... ... ... ...\n", + "86802 1588376760000 874.214 0\n", + "86803 1588376820000 937.929 0\n", + "86804 1588376880000 1031.279 0\n", + "86805 1588376940000 1099.698 0\n", + "86806 1588377000000 935.405 0\n", + "\n", + "[86807 rows x 3 columns]\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "df = pd.read_csv(os.path.join(\"..\", \"data\", \"example.csv\"))\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The column `timestamp_millis` consists of Unix timestamps (in units of milliseconds), and the column `kpi` contains the value of the time series metric at each of those timestamps. We will also create a version of this dataframe that is indexed by time:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " kpi kpi_label\n", + "timestamp_millis \n", + "2020-03-02 09:12:00 667.118 0\n", + "2020-03-02 09:13:00 611.751 0\n", + "2020-03-02 09:14:00 599.456 0\n", + "2020-03-02 09:15:00 621.446 0\n", + "2020-03-02 09:16:00 1418.234 0\n", + "... ... ...\n", + "2020-05-01 23:46:00 874.214 0\n", + "2020-05-01 23:47:00 937.929 0\n", + "2020-05-01 23:48:00 1031.279 0\n", + "2020-05-01 23:49:00 1099.698 0\n", + "2020-05-01 23:50:00 935.405 0\n", + "\n", + "[86807 rows x 2 columns]\n" + ] + } + ], + "source": [ + "time_idx_df = df.copy()\n", + "time_idx_df[\"timestamp_millis\"] = pd.to_datetime(time_idx_df[\"timestamp_millis\"], unit=\"ms\")\n", + "time_idx_df = time_idx_df.set_index(\"timestamp_millis\")\n", + "print(time_idx_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## UnivariateTimeSeries: The Basic Building Block\n", + "\n", + "The most transparent way to initialize a `UnivariateTimeSeries` is to use its constructor. The constructor takes two arguments: `time_stamps`, a list of Unix timestamps (in units of seconds) or datetime objects, and `values`, a list of the actual time series values. You may optionally provide a name as well." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from merlion.utils import UnivariateTimeSeries\n", + "\n", + "kpi = UnivariateTimeSeries(\n", + " time_stamps=df.timestamp_millis/1000, # timestamps in units of seconds\n", + " values=df.kpi, # time series values\n", + " name=\"kpi\" # optional: a name for this univariate\n", + ")\n", + "\n", + "kpi_label = UnivariateTimeSeries(\n", + " time_stamps=df.timestamp_millis/1000, # timestamps in units of seconds\n", + " values=df.kpi_label # time series values\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you may initialize a `UnivariateTimeSeries` directly from a time-indexed `pd.Series`:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Are the two UnivariateTimeSeries equal? True\n" + ] + } + ], + "source": [ + "kpi_equivalent = UnivariateTimeSeries.from_pd(time_idx_df.kpi)\n", + "print(f\"Are the two UnivariateTimeSeries equal? {(kpi == kpi_equivalent).all()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We implement the `UnivariateTimeSeries` as a `pd.Series` with a `DatetimeIndex`:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Is UnivariateTimeSeries an instance of pd.Series? True\n" + ] + } + ], + "source": [ + "print(f\"Is {type(kpi).__name__} an instance of pd.Series? \"\n", + " f\"{isinstance(kpi, pd.Series)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time\n", + "2020-03-02 09:12:00 667.118\n", + "2020-03-02 09:13:00 611.751\n", + "2020-03-02 09:14:00 599.456\n", + "2020-03-02 09:15:00 621.446\n", + "2020-03-02 09:16:00 1418.234\n", + " ... \n", + "2020-05-01 23:46:00 874.214\n", + "2020-05-01 23:47:00 937.929\n", + "2020-05-01 23:48:00 1031.279\n", + "2020-05-01 23:49:00 1099.698\n", + "2020-05-01 23:50:00 935.405\n", + "Name: kpi, Length: 86807, dtype: float64\n" + ] + } + ], + "source": [ + "print(kpi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also convert a `UnivariateTimeSeries` back to a regular `pd.Series` as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "type(kpi.to_pd()) = \n" + ] + } + ], + "source": [ + "print(f\"type(kpi.to_pd()) = {type(kpi.to_pd())}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can access the timestamps (either as timestamps or datetime objects) and values independently:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1583140320.0, 1583140380.0, 1583140440.0, 1583140500.0, 1583140560.0]\n" + ] + } + ], + "source": [ + "# Get the Unix timestamps (first 5 for brevity)\n", + "print(kpi.time_stamps[:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DatetimeIndex(['2020-03-02 09:12:00', '2020-03-02 09:13:00',\n", + " '2020-03-02 09:14:00', '2020-03-02 09:15:00',\n", + " '2020-03-02 09:16:00'],\n", + " dtype='datetime64[ns]', name='time', freq=None)\n" + ] + } + ], + "source": [ + "# Get the datetimes (this is just the index of the UnivariateTimeSeries,\n", + "# since we inherit from pd.Series)\n", + "print(kpi.index[:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[667.118, 611.751, 599.456, 621.446, 1418.234]\n" + ] + } + ], + "source": [ + "# Get the values\n", + "print(kpi.values[:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may index into a `UnivariateTimeSeries` to obtain a tuple of `(timestamp, value)`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "kpi[0] = (1583140320.0, 667.118)\n" + ] + } + ], + "source": [ + "print(f\"kpi[0] = {kpi[0]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you instead use a slice index, you will obtain a new `UnivariateTimeSeries`:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "type(kpi[1:5]) = \n", + "\n", + "kpi[1:5] = \n", + "\n", + "time\n", + "2020-03-02 09:13:00 611.751\n", + "2020-03-02 09:14:00 599.456\n", + "2020-03-02 09:15:00 621.446\n", + "2020-03-02 09:16:00 1418.234\n", + "Name: kpi, dtype: float64\n" + ] + } + ], + "source": [ + "print(f\"type(kpi[1:5]) = {type(kpi[1:5])}\\n\")\n", + "print(f\"kpi[1:5] = \\n\\n{kpi[1:5]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Iterating over a `UnivaraiateTimeSeries` will iterate over tuples of `(timestamp, value)`:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1583140320.0, 667.118)\n", + "(1583140380.0, 611.751)\n", + "(1583140440.0, 599.456)\n", + "(1583140500.0, 621.446)\n", + "(1583140560.0, 1418.234)\n" + ] + } + ], + "source": [ + "for t, x in kpi[:5]:\n", + " print((t, x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TimeSeries: Merlion's Standard Data Class\n", + "\n", + "Because Merlion is a general-purpose library that handles both univariate and multivariate time series, our standard data class is `TimeSeries`. This class acts as a wrapper around a collection of `UnivariateTimeSeries`. We choose this format rather than a vector-based approach because this approach is much more robust to missing values, or different univariates being sampled at different rates.\n", + "\n", + "The most transparent way to initialize a `TimeSeries` is with its constructor, which takes a collection (list or (ordered) dictionary) of `UnivariateTimeSeries` its only argument:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import OrderedDict\n", + "from merlion.utils import TimeSeries\n", + "\n", + "time_series_list = TimeSeries(univariates=[kpi.copy(), kpi_label.copy()])\n", + "time_series_dict = TimeSeries(\n", + " univariates=OrderedDict([(\"kpi_renamed\", kpi.copy()),\n", + " (\"kpi_label\", kpi_label.copy())]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you may initialize a `TimeSeries` from a `pd.DataFrame` and convert a `TimeSeries` to a `pd.DataFrame` as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "type(TimeSeries.from_pd(time_idx_df)) = \n", + "\n", + "(recovered_time_idx_df == time_idx_df).all()\n", + "kpi True\n", + "kpi_label True\n", + "dtype: bool\n" + ] + } + ], + "source": [ + "time_series = TimeSeries.from_pd(time_idx_df)\n", + "print(f\"type(TimeSeries.from_pd(time_idx_df)) = {type(time_series)}\\n\")\n", + "\n", + "recovered_time_idx_df = time_series.to_pd()\n", + "print(\"(recovered_time_idx_df == time_idx_df).all()\")\n", + "print((recovered_time_idx_df == time_idx_df).all())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We may access the names of the individual univariates with `time_series.names`, access a specific univariate via `time_series.univariates[name]`, and iterate over univariates by iterating `for univariate in time_series.univariates`. Concretely:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['kpi', 'kpi_label']\n" + ] + } + ], + "source": [ + "# When we use a list of univariates, we retain the names of the univariates\n", + "# where possible. If a univariate is unnamed, we set its name to its integer\n", + "# index in the list of all univariates given. Here, kpi_label was\n", + "# originally unnamed, so we set its name to 1\n", + "print(time_series_list.names)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['kpi_renamed', 'kpi_label']\n" + ] + } + ], + "source": [ + "# If we pass a dictionary instead of a list, all univariates will have\n", + "# their specified names. The order is retained from the OrderedDict.\n", + "print(time_series_dict.names)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# We can access the KPI like so:\n", + "kpi1 = time_series_list.univariates[\"kpi\"]\n", + "kpi2 = time_series_dict.univariates[\"kpi_renamed\"]\n", + "\n", + "# kpi1 and kpi2 are the same univariate, just with different names\n", + "assert (kpi1 == kpi2).all()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time\n", + "2020-03-02 09:12:00 667.118\n", + "2020-03-02 09:13:00 611.751\n", + "2020-03-02 09:14:00 599.456\n", + "2020-03-02 09:15:00 621.446\n", + "2020-03-02 09:16:00 1418.234\n", + " ... \n", + "2020-05-01 23:46:00 874.214\n", + "2020-05-01 23:47:00 937.929\n", + "2020-05-01 23:48:00 1031.279\n", + "2020-05-01 23:49:00 1099.698\n", + "2020-05-01 23:50:00 935.405\n", + "Name: kpi_renamed, Length: 86807, dtype: float64\n", + "\n", + "time\n", + "2020-03-02 09:12:00 0.0\n", + "2020-03-02 09:13:00 0.0\n", + "2020-03-02 09:14:00 0.0\n", + "2020-03-02 09:15:00 0.0\n", + "2020-03-02 09:16:00 0.0\n", + " ... \n", + "2020-05-01 23:46:00 0.0\n", + "2020-05-01 23:47:00 0.0\n", + "2020-05-01 23:48:00 0.0\n", + "2020-05-01 23:49:00 0.0\n", + "2020-05-01 23:50:00 0.0\n", + "Name: kpi_label, Length: 86807, dtype: float64\n", + "\n" + ] + } + ], + "source": [ + "# We can iterate over all univariates like so:\n", + "for univariate in time_series_dict.univariates:\n", + " print(univariate)\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Univariate kpi_renamed\n", + "time\n", + "2020-03-02 09:12:00 667.118\n", + "2020-03-02 09:13:00 611.751\n", + "2020-03-02 09:14:00 599.456\n", + "2020-03-02 09:15:00 621.446\n", + "2020-03-02 09:16:00 1418.234\n", + " ... \n", + "2020-05-01 23:46:00 874.214\n", + "2020-05-01 23:47:00 937.929\n", + "2020-05-01 23:48:00 1031.279\n", + "2020-05-01 23:49:00 1099.698\n", + "2020-05-01 23:50:00 935.405\n", + "Name: kpi_renamed, Length: 86807, dtype: float64\n", + "\n", + "Univariate kpi_label\n", + "time\n", + "2020-03-02 09:12:00 0.0\n", + "2020-03-02 09:13:00 0.0\n", + "2020-03-02 09:14:00 0.0\n", + "2020-03-02 09:15:00 0.0\n", + "2020-03-02 09:16:00 0.0\n", + " ... \n", + "2020-05-01 23:46:00 0.0\n", + "2020-05-01 23:47:00 0.0\n", + "2020-05-01 23:48:00 0.0\n", + "2020-05-01 23:49:00 0.0\n", + "2020-05-01 23:50:00 0.0\n", + "Name: kpi_label, Length: 86807, dtype: float64\n", + "\n" + ] + } + ], + "source": [ + "# We can also iterate over all univariates & names like so:\n", + "for name, univariate in time_series_dict.items():\n", + " print(f\"Univariate {name}\")\n", + " print(univariate)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Time Series Indexing & Alignment\n", + "\n", + "An important concept of `TimeSeries` in Merlion is _alignment_. We call a time series _aligned_ if all of its univariates are sampled at the same time stamps. We illustrate examples of time series that are and aren't aligned below:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Is aligned? True\n" + ] + } + ], + "source": [ + "aligned = TimeSeries({\"kpi\": kpi.copy(), \"kpi_label\": kpi_label.copy()})\n", + "print(f\"Is aligned? {aligned.is_aligned}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Is aligned? False\n" + ] + } + ], + "source": [ + "not_aligned = TimeSeries({\"kpi\": kpi[1:], # 2020-03-02 09:13:00 to 2020-05-01 23:50:00\n", + " \"kpi_label\": kpi_label[:-1]}) # 2020-03-02 09:12:00 to 2020-05-01 23:49:00\n", + "print(f\"Is aligned? {not_aligned.is_aligned}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If your time series is aligned, you may use an integer index to obtain a tuple `(timestamp, (value_1, ..., value_k))`, or a slice index to obtain a sub-`TimeSeries`:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1583140320.0, [667.118, 0.0])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aligned[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "type(aligned[1:5]) = \n", + "\n", + "aligned[1:5] = \n", + " kpi kpi_label\n", + "time \n", + "2020-03-02 09:13:00 611.751 0.0\n", + "2020-03-02 09:14:00 599.456 0.0\n", + "2020-03-02 09:15:00 621.446 0.0\n", + "2020-03-02 09:16:00 1418.234 0.0\n" + ] + } + ], + "source": [ + "print(f\"type(aligned[1:5]) = {type(aligned[1:5])}\\n\")\n", + "print(f\"aligned[1:5] = \\n{aligned[1:5]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may also iterate over an aligned time series as `for timestamp, (value_1, ..., value_k) in time_series`:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1583140320.0, (667.118, 0.0))\n", + "(1583140380.0, (611.751, 0.0))\n", + "(1583140440.0, (599.456, 0.0))\n", + "(1583140500.0, (621.446, 0.0))\n", + "(1583140560.0, (1418.234, 0.0))\n" + ] + } + ], + "source": [ + "for t, (x1, x2) in aligned[:5]:\n", + " print((t, (x1, x2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that Merlion will throw an error if you try to do any of these things with a time series that isn't aligned! For example," + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RuntimeError: The univariates comprising this time series are not aligned (they have different time stamps), but alignment is required to index into the time series.\n" + ] + } + ], + "source": [ + "try:\n", + " not_aligned[0]\n", + "except RuntimeError as e:\n", + " print(f\"{type(e).__name__}: {e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can still get the length/shape of a misaligned time series, but Merlion will emit a warning." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/abhatnagar/Desktop/Merlion/merlion/utils/time_series.py:672: UserWarning: The univariates comprising this time series are not aligned (they have different time stamps). The length returned is equal to the length of the _union_ of all time stamps present in any of the univariates.\n", + " warnings.warn(warning)\n", + "The univariates comprising this time series are not aligned (they have different time stamps). The length returned is equal to the length of the _union_ of all time stamps present in any of the univariates.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "86807\n" + ] + } + ], + "source": [ + "print(len(not_aligned))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The univariates comprising this time series are not aligned (they have different time stamps). The length returned is equal to the length of the _union_ of all time stamps present in any of the univariates.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 86807)\n" + ] + } + ], + "source": [ + "print(not_aligned.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, you may call `time_series.align()` to automatically resample the individual univariates of a time series to make it aligned. By default, this will take the union of all the time stamps present in any of the individual univariates, but this is customizable." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Is not_aligned.align() aligned? True\n" + ] + } + ], + "source": [ + "print(f\"Is not_aligned.align() aligned? {not_aligned.align().is_aligned}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TimeSeries: A Few Useful Features\n", + "\n", + "We provide much more information on the `merlion.utils.time_series.TimeSeries` class in the API docs, but we highlight two more useful features here. These work regardless of whether a time series is aligned!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may obtain the subset of a time series between times `t0` and `tf` by calling `time_series.window(t0, tf)`. `t0` and `tf` may be any reasonable format of datetime, or a Unix timestamp." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " kpi kpi_label\n", + "time \n", + "2020-03-05 12:00:00 1166.819 0.0\n", + "2020-03-05 12:01:00 1345.504 0.0\n", + "2020-03-05 12:02:00 1061.391 0.0\n", + "2020-03-05 12:03:00 1260.874 0.0\n", + "2020-03-05 12:04:00 1202.009 0.0\n", + "... ... ...\n", + "2020-03-31 23:55:00 1154.397 0.0\n", + "2020-03-31 23:56:00 1270.292 0.0\n", + "2020-03-31 23:57:00 1160.761 0.0\n", + "2020-03-31 23:58:00 1082.076 0.0\n", + "2020-03-31 23:59:00 1167.297 0.0\n", + "\n", + "[38160 rows x 2 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aligned.window(\"2020-03-05 12:00:00\", pd.Timestamp(year=2020, month=4, day=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " kpi kpi_label\n", + "time \n", + "2020-03-02 09:12:00 NaN 0.0\n", + "2020-03-02 09:13:00 611.751 0.0\n", + "2020-03-02 09:14:00 599.456 0.0\n", + "2020-03-02 09:15:00 621.446 0.0\n", + "2020-03-02 09:16:00 1418.234 0.0\n", + "... ... ...\n", + "2020-03-03 09:07:00 1132.564 0.0\n", + "2020-03-03 09:08:00 1087.037 0.0\n", + "2020-03-03 09:09:00 984.432 0.0\n", + "2020-03-03 09:10:00 1085.008 0.0\n", + "2020-03-03 09:11:00 1020.937 0.0\n", + "\n", + "[1440 rows x 2 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Note that the first value of the KPI (which is missing in not_aligned) is NaN\n", + "not_aligned.window(1583140320, 1583226720)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may also bisect a time series into a left and right portion, at any timestamp." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Left\n", + " kpi kpi_label\n", + "time \n", + "2020-03-02 09:12:00 667.118 0.0\n", + "2020-03-02 09:13:00 611.751 0.0\n", + "2020-03-02 09:14:00 599.456 0.0\n", + "2020-03-02 09:15:00 621.446 0.0\n", + "2020-03-02 09:16:00 1418.234 0.0\n", + "... ... ...\n", + "2020-04-30 23:55:00 1296.091 0.0\n", + "2020-04-30 23:56:00 1323.743 0.0\n", + "2020-04-30 23:57:00 1203.672 0.0\n", + "2020-04-30 23:58:00 1278.720 0.0\n", + "2020-04-30 23:59:00 1217.877 0.0\n", + "\n", + "[85376 rows x 2 columns]\n", + "\n", + "\n", + "Right\n", + " kpi kpi_label\n", + "time \n", + "2020-05-01 00:00:00 1381.110 0.0\n", + "2020-05-01 00:01:00 1807.039 0.0\n", + "2020-05-01 00:02:00 1833.385 0.0\n", + "2020-05-01 00:03:00 1674.412 0.0\n", + "2020-05-01 00:04:00 1683.194 0.0\n", + "... ... ...\n", + "2020-05-01 23:46:00 874.214 0.0\n", + "2020-05-01 23:47:00 937.929 0.0\n", + "2020-05-01 23:48:00 1031.279 0.0\n", + "2020-05-01 23:49:00 1099.698 0.0\n", + "2020-05-01 23:50:00 935.405 0.0\n", + "\n", + "[1431 rows x 2 columns]\n", + "\n" + ] + } + ], + "source": [ + "left, right = aligned.bisect(\"2020-05-01\")\n", + "print(f\"Left\\n{left}\\n\")\n", + "print()\n", + "print(f\"Right\\n{right}\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please refer to the API docs on `UnivariateTimeSeries` and `TimeSeries` for more information." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/v2.0.2/tutorials/advanced/1_AutoSARIMA_forecasting_tutorial.html b/v2.0.2/tutorials/advanced/1_AutoSARIMA_forecasting_tutorial.html new file mode 100644 index 000000000..d1a50f465 --- /dev/null +++ b/v2.0.2/tutorials/advanced/1_AutoSARIMA_forecasting_tutorial.html @@ -0,0 +1,804 @@ + + + + + + Tutorial for AutoSARIMA Forecasting Model — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

Tutorial for AutoSARIMA Forecasting Model

+

This notebook provides an advanced example on how to use the Auto Sarima forecasting model.

+

AutoSARIMA runs in 3 settings: 1. Full AutoSARIMA with approximation 2. Full AutoSARIMA without approximation 3. Partial AutoSARIMA (Predefined AR, MA, Seasonal AR, Seasonal MA hyper-parameters)

+

Example codes are provided for both cases below.

+
+

Prepare dataset

+
+
[1]:
+
+
+
import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+import logging
+
+from merlion.utils.time_series import TimeSeries
+from merlion.evaluate.forecast import ForecastMetric
+from merlion.models.automl.autosarima import AutoSarima, AutoSarimaConfig
+from merlion.models.forecast.sarima import Sarima
+
+from ts_datasets.forecast import M4
+
+logging.basicConfig(level=logging.INFO)
+
+time_series, metadata = M4("Hourly")[0]
+train_data = TimeSeries.from_pd(time_series[metadata.trainval])
+test_data = TimeSeries.from_pd(time_series[~metadata.trainval])
+
+# Visualize the time series and draw a dotted line to indicate the train/test split
+fig = plt.figure(figsize=(10, 6))
+ax = fig.add_subplot(111)
+ax.plot(time_series)
+ax.axvline(metadata[metadata.trainval].index[-1], ls="--", lw="2", c="k")
+plt.show()
+
+# Print the length of training data and test data
+print(f"{len(train_data)} points in train split, "
+      f"{len(test_data)} points in test split.")
+
+
+
+
+
+
+
+
+100%|██████████| 414/414 [00:00<00:00, 799.18it/s]
+
+
+
+
+
+
+../../_images/tutorials_advanced_1_AutoSARIMA_forecasting_tutorial_2_1.png +
+
+
+
+
+
+
+700 points in train split, 48 points in test split.
+
+
+
+
+

Train a full AutoSarima model with approximation (suggested, default)

+
+
[2]:
+
+
+
# Specify the configuration of AutoSarima with approximation
+# By default, approximation is only used if the time series is long enough
+#
+# p, q, P, Q refer to the AR, MA, seasonal AR, and seasonal MA params, so
+# auto_pqPQ=True (default) means select them automatically
+#
+# d is the difference order, and D is the seasonal difference order, so
+# auto_d=True (default) and auto_D=True (default) means select them automatically
+#
+# auto_seasonality=True (default) means to automatically select the seasonality
+config1 = AutoSarimaConfig(auto_pqPQ=True, auto_d=True, auto_D=True, auto_seasonality=True,
+                           approximation=True, maxiter=5)
+model1  = AutoSarima(config1)
+
+# Model training
+train_pred, train_err = model1.train(train_data)
+
+
+
+
+
+
+
+
+INFO:merlion.models.automl.seasonality:Automatically detect the periodicity is [24]
+INFO:merlion.models.automl.autosarima:Seasonal difference order is 1
+INFO:merlion.models.automl.autosarima:Difference order is 0
+INFO:merlion.models.automl.autosarima:Fitting models using approximations(approx_iter is 1) to speed things up
+INFO:merlion.models.automl.autosarima:Best model:  SARIMA(2,0,2)(0,1,1)[24] without constant
+
+
+
+
[3]:
+
+
+
# Model forecasting
+forecast1, stderr1 = model1.forecast(len(test_data))
+
+# Model evaluation
+smape1 = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=forecast1)
+print(f"Full AutoSarima with approximation sMAPE is {smape1:.4f}")
+
+
+
+
+
+
+
+
+Full AutoSarima with approximation sMAPE is 3.4491
+
+
+
+
[4]:
+
+
+
# Visualize the groud truth, actual forecast and confidence interval
+fig, ax = model1.plot_forecast(time_series=test_data, plot_forecast_uncertainty=True)
+plt.show()
+
+
+
+
+
+
+
+../../_images/tutorials_advanced_1_AutoSARIMA_forecasting_tutorial_6_0.png +
+
+
+
+

Train a full AutoSarima model without approximation (slower)

+
+
[5]:
+
+
+
# Specify the configuration of full AutoSarima without approximation
+# Note that the default values of all the auto_* parameters are True
+config2 = AutoSarimaConfig(approximation=False, maxiter=5)
+model2  = AutoSarima(config2)
+
+# Model training
+train_pred, train_err = model2.train(train_data)
+
+
+
+
+
+
+
+
+INFO:merlion.models.automl.seasonality:Automatically detect the periodicity is [24]
+INFO:merlion.models.automl.autosarima:Seasonal difference order is 1
+INFO:merlion.models.automl.autosarima:Difference order is 0
+INFO:merlion.models.automl.autosarima:Best model:  SARIMA(2,0,3)(1,1,1)[24] without constant
+
+
+
+
[6]:
+
+
+
# Model forecasting
+forecast2, stderr2 = model2.forecast(len(test_data))
+
+# Model evaluation
+smape2 = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=forecast2)
+print(f"Full AutoSarima without approximation sMAPE is {smape2:.4f}")
+
+
+
+
+
+
+
+
+Full AutoSarima without approximation sMAPE is 3.6991
+
+
+
+
[7]:
+
+
+
# Visualize the groud truth, actual forecast and confidence interval
+fig, ax = model2.plot_forecast(time_series=test_data, plot_forecast_uncertainty=True)
+plt.show()
+
+
+
+
+
+
+
+../../_images/tutorials_advanced_1_AutoSARIMA_forecasting_tutorial_10_0.png +
+
+
+
+

Train a partial autosarima model

+

Here, the user has pre-defined the AR, MA, Seasonal AR, and Seasonal MA hyper-parameters.

+
+
[8]:
+
+
+
# Specify the configuration of partial AutoSarima
+# We explicitly specify values of p, q, P, Q in the order and seasonal order,
+# and we set auto_pqPQ=False.
+# Because auto_d=True, auto_D=True, and auto_seasonality=True by default, we
+# can specify arbitrary values for them in the order and seasonal order (e.g. "auto")
+config3 = AutoSarimaConfig(auto_pqPQ=False, order=(15, "auto", 5),
+                           seasonal_order=(2, "auto", 1, "auto"), maxiter=5)
+model3  = AutoSarima(config3)
+
+# Model training
+train_pred, train_err = model3.train(
+    train_data, train_config={"enforce_stationarity": True,"enforce_invertibility": True})
+
+
+
+
+
+
+
+
+INFO:merlion.models.automl.seasonality:Automatically detect the periodicity is [24]
+INFO:merlion.models.automl.autosarima:Seasonal difference order is 1
+INFO:merlion.models.automl.autosarima:Difference order is 0
+
+
+
+
[9]:
+
+
+
# Model forecasting
+forecast3, stderr3 = model3.forecast(len(test_data))
+
+# Model evaluation
+smape3 = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=forecast3)
+print(f"Partial AutoSarima without approximation sMAPE is {smape3:.4f}")
+
+
+
+
+
+
+
+
+Partial AutoSarima without approximation sMAPE is 3.5288
+
+
+
+
[10]:
+
+
+
# Visualize the groud truth, actual forecast and confidence interval
+fig, ax = model3.plot_forecast(time_series=test_data, plot_forecast_uncertainty=True)
+plt.show()
+
+
+
+
+
+
+
+../../_images/tutorials_advanced_1_AutoSARIMA_forecasting_tutorial_14_0.png +
+
+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/advanced/1_AutoSARIMA_forecasting_tutorial.ipynb b/v2.0.2/tutorials/advanced/1_AutoSARIMA_forecasting_tutorial.ipynb new file mode 100644 index 000000000..046d79101 --- /dev/null +++ b/v2.0.2/tutorials/advanced/1_AutoSARIMA_forecasting_tutorial.ipynb @@ -0,0 +1,361 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial for AutoSARIMA Forecasting Model\n", + "\n", + "This notebook provides an advanced example on how to use the Auto Sarima forecasting model.\n", + "\n", + "AutoSARIMA runs in 3 settings:\n", + "1. Full AutoSARIMA with approximation \n", + "2. Full AutoSARIMA without approximation\n", + "3. Partial AutoSARIMA (Predefined AR, MA, Seasonal AR, Seasonal MA hyper-parameters)\n", + "\n", + "Example codes are provided for both cases below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 414/414 [00:00<00:00, 799.18it/s]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "700 points in train split, 48 points in test split.\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.stats import norm\n", + "import logging\n", + "\n", + "from merlion.utils.time_series import TimeSeries\n", + "from merlion.evaluate.forecast import ForecastMetric\n", + "from merlion.models.automl.autosarima import AutoSarima, AutoSarimaConfig\n", + "from merlion.models.forecast.sarima import Sarima\n", + "\n", + "from ts_datasets.forecast import M4\n", + "\n", + "logging.basicConfig(level=logging.INFO)\n", + "\n", + "time_series, metadata = M4(\"Hourly\")[0]\n", + "train_data = TimeSeries.from_pd(time_series[metadata.trainval])\n", + "test_data = TimeSeries.from_pd(time_series[~metadata.trainval])\n", + "\n", + "# Visualize the time series and draw a dotted line to indicate the train/test split\n", + "fig = plt.figure(figsize=(10, 6))\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(time_series)\n", + "ax.axvline(metadata[metadata.trainval].index[-1], ls=\"--\", lw=\"2\", c=\"k\")\n", + "plt.show()\n", + "\n", + "# Print the length of training data and test data\n", + "print(f\"{len(train_data)} points in train split, \"\n", + " f\"{len(test_data)} points in test split.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train a full AutoSarima model with approximation (suggested, default)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:merlion.models.automl.seasonality:Automatically detect the periodicity is [24]\n", + "INFO:merlion.models.automl.autosarima:Seasonal difference order is 1\n", + "INFO:merlion.models.automl.autosarima:Difference order is 0\n", + "INFO:merlion.models.automl.autosarima:Fitting models using approximations(approx_iter is 1) to speed things up\n", + "INFO:merlion.models.automl.autosarima:Best model: SARIMA(2,0,2)(0,1,1)[24] without constant\n" + ] + } + ], + "source": [ + "# Specify the configuration of AutoSarima with approximation\n", + "# By default, approximation is only used if the time series is long enough\n", + "#\n", + "# p, q, P, Q refer to the AR, MA, seasonal AR, and seasonal MA params, so\n", + "# auto_pqPQ=True (default) means select them automatically\n", + "#\n", + "# d is the difference order, and D is the seasonal difference order, so\n", + "# auto_d=True (default) and auto_D=True (default) means select them automatically\n", + "#\n", + "# auto_seasonality=True (default) means to automatically select the seasonality\n", + "config1 = AutoSarimaConfig(auto_pqPQ=True, auto_d=True, auto_D=True, auto_seasonality=True,\n", + " approximation=True, maxiter=5)\n", + "model1 = AutoSarima(config1)\n", + "\n", + "# Model training\n", + "train_pred, train_err = model1.train(train_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Full AutoSarima with approximation sMAPE is 3.4491\n" + ] + } + ], + "source": [ + "# Model forecasting\n", + "forecast1, stderr1 = model1.forecast(len(test_data))\n", + "\n", + "# Model evaluation\n", + "smape1 = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=forecast1)\n", + "print(f\"Full AutoSarima with approximation sMAPE is {smape1:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the groud truth, actual forecast and confidence interval \n", + "fig, ax = model1.plot_forecast(time_series=test_data, plot_forecast_uncertainty=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train a full AutoSarima model without approximation (slower)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:merlion.models.automl.seasonality:Automatically detect the periodicity is [24]\n", + "INFO:merlion.models.automl.autosarima:Seasonal difference order is 1\n", + "INFO:merlion.models.automl.autosarima:Difference order is 0\n", + "INFO:merlion.models.automl.autosarima:Best model: SARIMA(2,0,3)(1,1,1)[24] without constant\n" + ] + } + ], + "source": [ + "# Specify the configuration of full AutoSarima without approximation\n", + "# Note that the default values of all the auto_* parameters are True\n", + "config2 = AutoSarimaConfig(approximation=False, maxiter=5)\n", + "model2 = AutoSarima(config2)\n", + "\n", + "# Model training\n", + "train_pred, train_err = model2.train(train_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Full AutoSarima without approximation sMAPE is 3.6991\n" + ] + } + ], + "source": [ + "# Model forecasting\n", + "forecast2, stderr2 = model2.forecast(len(test_data))\n", + "\n", + "# Model evaluation\n", + "smape2 = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=forecast2)\n", + "print(f\"Full AutoSarima without approximation sMAPE is {smape2:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mS0KriUD+KwohhpxhGARiOs3hOIGoRoHXhs9hwWyS2RUhxOg43h6jOayS47IO2TEVRcFrN+O1m4moOrvrQ9jNCmlGHG+GjmMS5ymPdxIgCyGGhG4kFrk0h+LU+OPENAOzkrgl2RAMYTFBUZqNPI8Nt+TsCSFGUGtY5WBzmEzn8IU9DosJh8VETNM52KjSqHeQZjdTmJaYIHBZ5bw3nkiALIQYME038Mc0GoNx6gJx4pqOxaTgsZlJO+X2pceWeO7xjhiH26Kk281MTbeT4bRgNcsMixBi+ERVnb0NIbx284jcxbKZTWQ6TGS4rERUnQNNEQwD3DYTU7w2MpwWXFaT5CuPccN6ZfrpT39KeXk58+fP5yc/+QmQKA1y0UUXMWvWLC666CJaW1uBxC3Zz33uc5SWlrJw4UK2b98+nEMTQgyQphu0hFUONIV561gHO2oC1PljeKwmsl1WfA4LlhQXIbNJIcNhIcdlRdNhb2OIt475ebcpTEdUlUL8QoghdzLv2MAYlXQHh8VEltNCtsuCCahsCbOpys/G436OtEXwR7Vxee5zOp2sWLGi89+RI0dGe0gA3HfffYRCoSE51rB9Wvbu3cuvf/1rNm/ezK5du/jb3/7GwYMHufvuu7nggguorKzkggsu4O677wbgueeeo7KyksrKSu6//37uuOOO4RqaEGIAdMPg3aYwbx7rYGdtkMZgDK/NTJbLSno/84udVhPZTivpdjP1gRhbq4Nsrg5Q448S0/Rh/C2EEJPJybxjn330b5jbLSaynFZyXFZsZoWjbVG21AR465ifQy2JiYLxwul0smXLls5/JSUlfdpPVYf3d/zZz3429gPk/fv3s3LlSlwuFxaLhXPOOYennnqK9evXs27dOgDWrVvH008/DcD69eu55ZZbUBSFVatW0dbWRm1t7XANTwjRT9UdMY63J9Ijsl0W0uyDX3RnNimkO96fXaloivDmsQ72N0ep88cIxsbn7IoQYvR15h07Rj84Pp3NbCLDYSH7RLpFdUeMLdUBDrWE0cfpOW/Xrl2cddZZLFu2jBtvvLEzQ+Ciiy7iS1/6EqtXr+Z///d/2b59OxdeeCGrVq3iAx/4QGesd+jQIS699FKWL1/OypUrOXToEIFAgEsuuYSVK1eydOlSnnnmGQCCwSBXX301y5cvZ8mSJTz++OP87Gc/o6amhosvvpiLL7540L/PsH1qysvL+frXv05zczNOp5Nnn32W5cuXU19fT0FBAQD5+fnU19cDUF1dTXFxcef+RUVFVFdXdz73pPvvv5/7778fgMbGRhobGwc1zpP/AUXfyXvWPxPh/fLHdHY1REi3m2iPD1/enJnETHVdq5/msA4YWE0KWU4zGQ4zHpsJu1ny9k43ET5jI0ner/4bb+9ZTDPY0RDBZlboGMZzVnc6Ovz93sdkGOw62s7xejOzMmzYujnXGYbRORP75WffZU9t/1+rJwsKvPzo8tk9PiccDrN8+XIASkpKeOSRR7j11lu55557OOuss/je977Hd7/7XX70ox9hGAbRaJQ33niDeDzOJZdcwqOPPkpOTg5PPPEE3/zmN/nlL3/Jrbfeyp133slVV11FJBJB13UsFgt//vOfSUtLo6mpifPOO4/LLruM5557jvz8fJ588kkA2tvbSU9P56c//SnPPvss2dnZKWerNU3rc9w4bAHy3Llz+epXv8rFF1+M2+1m8eLFmM1dV3AqitLvJPXbb7+d22+/HYBFixaRk5Mz6LEOxTEmG3nP+mc8v19xTaeyOkBhtgundWRy+EyKQoYvA0jkPIfiOrWaDmEFp9VErstChtOK125Ome88GY3nz9hokPer/8bLe6YbBrvrgqSl20c1teLkOaw/soC2iMrRmMKCPDdee3Lli+bmZiyWxO9lGkAc1RuTonQevztOp5OtW7d2/tze3k5HRwfnnXcekGg9/eEPfxiLxYKiKNx0001YLBYOHDjAvn37uOqqq4BEwJqfn084HKa2tpbrrrsOAI/HA0A8Huc73/kOb775JiaTiZqaGpqbm1m4cCFf+9rX+M///E8uv/xyzjzzTCARV1oslm7HbzabycrK6tP7MKyfnE9+8pN88pOfBOBrX/saRUVF5OXlUVtbS0FBAbW1teTm5gIwZcoUjh8/3rlvVVUVU6ZMGc7hCSH64GBLhJhmkOkc+hJFkUiEhsZGGurrqW9ooKG+nta2VoqKipg1axalpaV4Pd4TF4nE68c0naqOGEfbYhiAz2km120l123FJhUxhJj0hqPe8UjyOSyE4zpbqv3MzXFR4LV1+9x7rpw7giMbOLfbDSRmv+fNm8frr7/eZbvfn3oW/M9//jNNTU1s2rQJq9XK7NmziUQizJ49m02bNvH888/z7W9/m/POO4+vf/3rQzrmYQ2QGxoayM3N5dixYzz11FNs2rSJw4cP89BDD3HXXXfx0EMPcfXVVwNw1VVX8bOf/YwPfvCDvP3226SnpyelVwghRlZDIEZ1R4ycQXSG0nSd5uZmGhoaaGiop6G+gfqGBhobGgiGQuTm5JCbl0debi7zy+eTnu7jyJEjbNq4iT/+8U9kZGQwe/YsZs2aTWlpKR63uzMQNgyDqGbwblOEY21RyvNcpI2BxThCiNExEvWOR4LTasJqVninIYQ/qjEz0zGmGy2lp6fj8/l48803OfPMM/nTn/7EWWedlfS82bNn09jYyKZNm1i1ahXxeJzKykrmzZvHlClTWL9+PVdffTXRaBRN02hvbycnJwer1cqrr77K0aNHAaipqSEzM5MPf/jDpKen8+CDDwKJmWe/3092dvagf6dh/QRdf/31NDc3Y7Va+fnPf47P5+Ouu+7ipptu4oEHHmDatGk89thjAFx++eU8++yzlJaW4nK5On9ZIcToCMU19jWF8TnMA7qFV9/QwOOPPcbBQ4dIS/OSm5tHbm4OBYWFLF68mNy8PHw+H6YUx87NzeGSiy9G03WOHz9OZeW7vPXWW/zh978nOzubWbNnM3v2LGbMmInb5cJhMZ2YcQkyJ8vBlDSb1BgVYpKJjHC94+FmMSnkuCxUd8QIxDTm5brGdGe+Bx54gM985jOEQiGmT5/Or3/966Tn2Gw2HnnkEb74xS/S3t6Oqqp89rOfZd68efzmN7/h85//PN/97nexWq386U9/4kMf+hDXXXcdS5cuZdmyZcyZMwdIVEr7j//4D0wmE1arlf/93/8FEpkLV155JYWFhbz44ouD+n0UYxwvEV+0aBHPP//8oI7R2Ng4bvKqxgp5z/pnPL5fumGwszZIOK6nzIHriabr/PMf/+Dll1/m0ksvZc3atdis/bvV2drWmjJ/T9U0jh07xrvvvsvBykqOHDlCTm4uq1at4pyzz+6s0ZzrsTIn2zlpUi7G42dsNMn71X9j/T07mXfcEdPGREm37s5hA9URVdENWJjvRgu2M3t2z4voxiNVVXvNfR6sd999NykH+corr+yST33S6H+KhBBjzrH2KG1RlWxn/wLbmtpa/viHP+BwOLjzy18mu4+LIfrKYjYzY/p0ZkyfDpdcgqqqHDlyhAd++1tmzZpFYUEBOW4rLWGVrdUBSbkQYpIY73nHvUmzW4ioOttqApR7x+285rgyOaZXhBB91hZROdQcIaMfgaWqaTz3/HPc99OfsmbtWj7z2c8MeXCcisViobS0lIsuupC//vWvnY9nOCyYFYUt1UGq2qNSS1mICWws1zseSg6LiUyHhbhuEFGlodJwm9ifJiFEv8Q0nXfq+5fDd7zqOH/8wx9JS0/jq3d9dUhvK/bVWWedxauvvsrBQ4conTkTSCxysZkVDjSFaY2okyrlQojJYqLlHffGbFIwK4lztW4kguZJ8GsPif5OlMjVQggBJE4elc0R4obRp4UgcTXOX//2V37+819w3vnnc8cdd4xKcAxgtVi5/PIP8Mwz6zF4/yRoNimdKRdbqgK0R8ZPK1chRM8Mw+BAUxiDvp2zJgrVMBHsaEM/USNelxtkvTIMg9bW1qR+HD2RGWQhBAB1gTi1/r6VdDty5Ah/+OMfyc3N4a677sKXnj4CI+zZGWecwSuvvMLevXtZUL6gy7YMRyJ/b2tNkNlZDoqkyoUQ415HVKMppA6qDOV41KbboLGF5qYmTk6K2sxD3zBkpGma1q8Atr/MZjNer7fPz59cnyohREqhmEZFU4hMp6XHk2w0FuPZZ//O5s1buP7661m2bCkKY+OkbFIUrrrqSp555q/Mn1+eVD7OYTFhNSm82xSmLaIyO8uJfRLNOgkx0VR1RHFMwtbzhmKi1XBw8mZZe1TFYTaxqMA9rtPIGhsb+9zlbiSM33dSCDEkNN3gnYYQdrOpx7bNtXW1/PCHd9PW2sbXvvYfLF+2bMwExyeVl5fjdDrYvHlzyu2nplxsqwkQimsjPEIhxFAIx3XqA3E8Nglj0u0WQmriPK5KvsWQkU+WEJPckbYIgZiOx9bzra2///1ZVq1axa233orX0/fbVCNJQeGqq67m2Wf/TlyNd/u8DIcFw4BddSGishpciHGnPhhDURj3aQVDJcNhpi2isb8xhC5Ve4aEBMhCTGItYZXDrVEynD0Hxx1+P+++e4Czzzp7hEY2cKUzZ1JYOIU33nijx+d57WZUXWdPQ5C4JkGyEOOFqhsca4uOiYYgY0mW00JDMM67TWEpbTkEJEAWYpKKqjrvNIRIt5tTtns+1ea332bhwkU4HI4RGt3gXHnllbz00stEIpEen5dutxCIJt4HTW5NCjEuNIfiqIYxKcq69Ve2M9Ga+r2WiATJgyQBshCT1KGWCLph9LpQzcDgrQ0bWLNmzQiNbPCmFBYyd24ZL7/ySq/PzXRaaIloVDSF5dakEGOcYRgcbo3itQ5ftYPxTFEUslwWjrRFOdYeG+3hjGsSIAsxCUVUnfpADJ+994tMZeVBLBYL06eXDP/AhtDll3+AN15/nQ6/v9fnZjst1AViVDbLrUkhxrK2iEYoro1IBZqYZrCzLsjTFS3UBrpf0zDWmBSFTKeFd5vD1Pijoz2ccUsSeISYhOoDcRSlb3UzN7z1FmvXrBlzFSt6k52VxYozzuD555/jphtv6v35TgtV7TGsJoUZmc4RGKEQor+Ot0eHrSmIgcHhtijba4LsqA2ypyFETEt8Yf7V1nrWTvVy/bws5maP/fOD2aSQ7bSwryGM1aSQ47aN9pDGHQmQhZhkNN3gWHuUtD7MHgeCQd7Zt48bb+o9wByLLrnkYv7re//FeeedR052To/PPXlr8nBbFJvZRFG6fYRGKYToi1BMozGkkt3LouL+aArF2VEbZHttkB11QdoiidKPRWk2Lin1sbTATXGanRcPtfHsu628eczP3GwH18/LZnWxB/MYrqJhNilkOCzsqQ+zpMBEhlNCvv6Qd0uISaY1rBLTdNL7ECBv3vw25eXluF2uPh8/rhvsqg+y6ZifrbUB8j02Li3NYE2xF9sIF/X3eryce965/P1vf+fjH/94r883KQqZDgsVTWEsZoV8j8y6CDFW1AZiWEyDK+0WVnV21SWC4e01QY53JPJ00+1mlhS4WZLvZkmBm1y3tct+n1iSy4cWZPPCwTaermjmv16vosBj5ZqyLC4pTR+zra6tZgWvzcTOugDLCj2kSeWPPpN3SohJ5nBbBE8fFrgYGGzYsJGbb+599jgY19laHWBjlZ/NVQFCqo7drLC4wM2R1gh3v1lNmt3MRTPSuWxWBkVpIxd4nnfueXz3e9/leNVxiouKe32+2ZTI33unIYRVUcg67UIphBh5cU3neHusT1/su1MfjPP55w7TFtGwmaE8180lpT4WF7iZkWHH1EsamdNi4pqyTK6ck8GGY36e3N/M/22t4/e7G/jA7AyumpNBlnPsnS/sFhMGsLM2yLJCD+5eat6LBAmQhZhE/FGN9oiWNDuSynvvHcbQdUpLS1Nub4mobDruZ8NxPzvrgqg6pNnNnDktjTXFHhbne3BYFHQMttcEea6ylb/sb+HJ/S0synNx2ayRmVV2OBxccsmlPPPMX/m3T3+6T/tYTArpdjO76hMXlHSHnCqFGE2NoTiGwYBLu6m6wQ/eqCKi6nzvvGIW5ruxD/DcY1YUzpqWxlnT0ninMcxT+5p4dG8zT77TzLnT07l+Xia+AR15+DgsJjTdYGdtkKWFHpzWsTnjPZbIWV+ISaTaH+3zRWHDhrdYs3Ztl8V5VR0xNhzvYMNxPxVNiRrD+R4rV83OZM1UL3NznEk5eSYUlhd6WF7ooTkc56VD7Tx/sJW736zGe8qscvEwziqvXbOGf/7jH7xbWcnsWbP6tI/NbMJjgx11QZYXenrtNCiEGB66YXCkNYrXPvCg7nc7G6hoinDXmVNYMcUzZGObn+Nk/jnF1ARi/GV/Cy8ebOPl99pZkw3LZ8LqYi8ZY+QLtttmpj2icrA5zIJ892gPZ8wbG//VhBDDLqrq1PhjZPbhZB0Kh9m9ew/XXHMNkEi3+PHGWl461A5AaaaDjy3KZk2xlxKfvc8VLrKcVj5Yns1N5Vlsrw3y/LttrK9o4an9LSzIdXLZ7AzWFqcN+HfsjsVi4YorruCZ9ev50p1f6vN4HRYTumGwozbIskI3Lqm9KsSIaw2rRFQdj21g6Qubqvw8sa+FK2b5OLdk6M8vAIUeG/+2Ip+PLczh2cpWNh2s4b6367jv7TrKc52snZrGmiIveZ7RTcFId1ioD8aZElbJlEV7PZJ3R4hJojEYR0HptWsewJYtW5g7twyvxwvACwfbeOlQO1fNyeD6eVnkDTIv14TC8gIPyws8tERUXjzYxvMH2/h/b9bgtdWxNlehfKrC0kJPnwL6vli6bBkvv/wyu3buYvHixX3ez2U1E4hpnbcmx+piHCEmqmPtUZwD/LurD8b5nw01zMxwcNvy/CEeWbI0u5kPlmdz8RQz7YqTt475eeuYn19tredXW+spzXRw5lQva4q9TB2lSjlpdjMHmsKcMcUj3Qh7IAGyEJOAbhgcbYuS1oc0AQODDW+9xbXXXQckLk7/t6Wexfku/nVFXq8LWfor02HpnFXeWRfipYNtVNQ28XxVLQDTfXaWFbpZWuhhfo5rwHmDJkXhyquu4qknn2TBwoWYTX2/4HpsZtqjKvsbQyzOdw9qFb0Qou8CMY2WsEqOq/9fyuMn8o413eBrZ08Z8LljIBQFpvscTPc5+OjCHKr9MTYc8/PW8Q5+t7OR3+1spDjNxtqpaayd6qE00zFiteYdFhNN4Ti1/piUs+yBBMhCTAKJW5QGXnvvJ+CjR48RjUaZPXs2Uc3gB29UY7eYuHNt4ZAHx6cyobA0383SfDctrS6aDQfbThTsf3p/C0/sa8FmVliY62JpoZtlBR6m+mz9uqjMmzeXl15KY9OmTaztZ+vsdLuFplCcxmCcXCn/JsSIqOlINO8ZiAd3JPKO/+PMKUzxju7f7BSvjRvnZ3Hj/CyaQnE2HA/w1rEOHt3bxCN7m8hzW7hyTibXzcsc1vPsST67hYOtEbLdVrkr1g0JkIWYBI62R3Hb+r44b/WaNZgUhd/uqONwW5TvnldE9giWL1IUmJXhZFamkw+WZxNWdXbXBdleF2RbTZD7tzUADWQ7LSwpdLO22MvKIk+vwbKCwtVXX8UDD/yWFStWYLP273dKs5s50Bwhw2nBapaLihDDKarqVPtjZDj6n/u/scrPU/tbuGK2j3OGKe94oLJdVq6akygL1xZR2VQV4NUj7fxmewPvNIS4c+0U3MNcZcJiSpwtD7dGmJvT9zr3k4mc4YWY4AIxjdaw2qcFZpFIhJ07d7Jy5Uo2VflZX9HKNWUZnDHFOwIj7Z7TYmJlkZc7lufzm6tm8tA1pXx+VT5lOU42HvPz7Ver+OGbNYRVvddjTS+ZzrSpU3nzzTf6PQ6b2YR6ohOhEGJ4NQbjAH1aN3Gq+kCcezbUUJrp4LZlw593PBg+h4VLS3384MKp/MvyPN6uCvC5Z9/jaNvwn2N8djPV/hgdUXXYX2s8kgBZiAmuP7cot23fzqzSWahWF/dsrGVmhoNPLMkb5hH2X57HymWlGXzj7CIeuWk2tyzK5rUjHXz22cMcbov0uv8FF17Im2++iYHR79fOcJg52hYjGNMGMnQhRB/0Z93EqeK6wQ/erELXGfG848FQULi2LJO7L5pGMK7z+ecP8+Yx//C+pqLgtph4tzmMYfT/XDjRSYAsxAQW0xK3KNP62H1qw1uJ9IofvVVDXNP5j7OmjHh76P6yKAofXpDDDy6cSiCm8fnnjvDCwbYe95k+vQSTycyhg4f6/XomRcFuVjjYEpGLihDD5OS6CWs/zz+/3Z7IO/731QUUjsO1AgvzXPzv5dMp8dn5r9ereGBHA+ownmfcNjPtYZ2GE7P14n0SIAsxgTUG4xj07RZlVVUV/oCf3VoOu+pDfHpF/oi2hB6sxflufnHFDOZmO7l3Uy3/s6GGSDcpFwoKq1evZuOmjQN6La/dTGMwTnNILipCDIfDrRE8tv6FKBuq/PylooUr52Rw1rSxlXfcHzkuK//vohI+MMvH4+80841XjtEWGb40iHSHiXebI8S13lPUJhMJkIWYoN6/Rdm3P/O3Nmxg6ryl/GF3M+eWpHHRzPRhHuHQy3RY+O8Lp/LhBdm8/F47n3vucLe5fCtWrGD37j2EI+EBvVa63cy7zRFUXWaRhRhKHVGV9qjWr3bIdYE4P37rRN7x0rGXFtZfNrPCZ1cW8IVVBbzTEOKzzx6msnlg56reX0vWVqQiAbIQE9TJ7lO2PlRbiESjbNm6lVfVqeR6LHxmZf6I1eQcamZF4ZZFOXz/gmLaIxqfe+4wL7/XnvS8NK+XObNns23b9gG9jt1iIqoaVHfEBjtkIcQpqtpj/codPlnvWDcSecdjPS2sPy4t9XHPJSUYwBdfOMKLh9qG5XVkbUUyCZCFmKCOd8T63H1qx47ttNhzacXJXWcW4ZkALZWXFXj4+RXTmZXl4H821HDvploiatfZ3tVr1rBxw4YBv4bPYeZQa5hQXC4qQgyFiKpTH4jh7cfivAe213OgOcIX1xSOy7zj3szOcvKzy6czP9fFjzfW8rO3a4kP8Z0rWVuRTAJkISagYEyjORTH3ceLzOPPv0pN+hxuWZRDWbZzmEc3crKdVu6+aBofLM/ihYNtfOH5wxw/ZcZ37ty5tHd0UFVdPaDjm00KVkXhvdbeK2cIIXpXH4ijKEqfu1W+ddzP0xWtXDUngzOnjm45yuHkc1j4rwumcsO8LP5W2caXXzxC0xCvgfDazTSF4rSEpewbSIAsxIRU6+9Habd3j7L/eAOLF5VzY3nWMI9s5FkUhY8vzuW/zi+mOazy2Wff459HEikXJkVh1cqVbNo4sMV6kGgeUheI0yoXFSEGRdMNjrZH+1x1py4Q58cbapiV6eBTEyDvuDcWReFTS3P52llTONIW5TPPHuZA09DmJXttZg40hdFkbYUEyEJMNHFNp6qjb7coo5rBPY++gH1qOV8+s2hEWpyOluWFHn7xgenMyHDwwzdr+NXWegwMVq1ezdatW4mrA5uNURSFNJuZCrmoCDEozaE4cU3H0scv9/e9XQvA184umlB5x705e1oaP7l0Og6Lwjf+cWxIm4o4TqytqPHL2goJkIWYYBpDcQwjcfu/N/dvqabl0F4+c8NFZI1gK+nRkiifNI0r52Twl4oWfr+rieysLIqKiti1a/eAj+uwmAirGrVyURFiwKr8sT6vfzjSFmV7bZAb52dR4Bm6c5eBQSgcpq6ujooDB9i8ZTOvv/E67R0dQ/YaQ6HEZ+cHF5ZgMZn4+ivHqA8MXbqFz2HmYEuEcHxyl32zjPYAhBBDxzAMjrRG8dp7/+67scrP31/bzPRpxVw4f+oIjG5ssJgUPr0ij6hq8Kc9TaTbLaxavZpNGzeyfNmyAR/XZ7dwsDVCttuKo4+LI4UQCTFNpy2ikuXoW1jydEULNjNcNiujX6/jD/ipra2jvb2N9vYO2tvb6Whvp729nbYT/2s2mUhPTyfdl056ug+Av/3tbyxevJjzz7+A/Lyxkc5R4LHy/QuL+fILR/naK0e555ISfH18/3piNimYlUQt6nm5riEY6fgkAbIQE0hbRCOi6nhsPc+oNIUSuXvehnf4xIc/MEKjGzsUFD63Kp+OqMr/ba3jzlXTqap6nKbmZrKzBpaHbTElCuMdbo0wN2fyXlSEGAh/VAODPi3Oa49q/ONwG+dP95Hex3xlgJbWVu75n/8hJyfnlAA4nWlTp5LuSyctLZ30tDQcDkfy+AJ+3njjDX76059SUlLC+eefR2lp6aiXw5zhc/Cd84r52itH+cY/jvPDi6bh7kf96O6k283U+GMUptmGJOgejybnby3EBHW8Pdqn2ctH9jYRbm9mtjnIkkULR2BkY49FUbjrzCK+8Y+j/OTtei6YvYC3336bD1x++YCP6bObqfbHKPTaSJ+kFxUhBqIxqGLvQ812gOcqW4lpcE1ZZp+PH4lGuf9Xv+L8Cy7ggvPP7/f4vB4vl192ORdeeBGb336bP/3pz7hdLs6/4HwWL17Sp26lw6U818XXzyriO69W8d1Xj/Pd86f2q450Koqi4LGZeLcpzPIpnlH9/UaL3AcUYoIIxTQaQ2qvsweBuMbL77UzI3CQs9eswmKZvIGcw6Lw7fOKKU638894MS+99ib6IGqAKoqC22Li3ebwoI4jxGRiGAaNoTiuPsx8qrrB3w60sjjfRYnP3qfj64bBH37/e4qKijj//PMGNVab1cqZZ57JN7/5TS686CL++c9X+e53vsNrr79OJDp6nehWFnn50tpCdtWH+OGbVahDcP5xWc34Y9qQ5jePJxIgCzFB1AViWEy936J86VA7kWgcU807rF6zZoRGN3Z5rGa+f+FUMvMKORgw8drWXYM6nttmpiMyeS8qQvRXIKYT14w+LSx+85ifprDar9nj559/jvaODm7+4M1DlhJhUhQWL1rEl774RW5Zt453Dxzg29/6Fn/729/o8PuH5DX664Lp6fzr8jw2HA/wv2/XYTD4INlnt1DZHJ6UC/YkQBZiAlB1g+MdMdJ6Ke2mGQbrK1ooUWspmVJAXm7uCI1wbMt0WPjvC6diL1nIfU+8TENwcMGtz5G4qMS0yXdREaK/2iIqitK3YO7pimYKPVbOKPL06fnbd2xn48ZNfOpTn8JqGZ5KPTOmT+e2227j37/4RQLBIP/1X9/jtVdfG5IAtb+uKcvkQ+XZvHCwjQd3NA76eFazgllR2FkXJKJOrvOZBMhCTACtYRXN6H0GZkt1gLpAnILAEZYuWzpCoxsfCj02vvvRi4jWHeE/nt1Pe3Tg7aOtZgXdgGNDWJ9UiImqLhDD3YfybhVNYSqaIlxVltmnmu3Hq47z2KOPcfvtt5GeljYUQ+1RXm4uH7z5Zr7xjW9y9Ngx/v63vw/7a6Zyy+JsLp/l47F3mnliX/Ogj+e1m9F1gx21kytIlgBZiAmgIRDD0YcFLs8caCXTZuCvqmTx4iXDOiZNN+iIqjSH4+NmJnV+QQbnrVpC3YHdfOufxwZ1MfA5zBxtjyZW5wshUoqqOv6o1qfFxU9XtOC0KFw009frczv8fn79619z0803UVxUPODx6YZBMKb161yQ5vVy/fXXs33Hdl5/4/UBv/ZAKSj82xn5nDXVy2+2N/DiobZBH3MyBskSIAsxzmm6QWNY7XWBy7H2RGH9ZdYGSqZNI83rHfKxxDSd1rBKU1jFH9fJcdsozXQS0wyaQnECMQ1jjC9eu+ais5kdPkRFU5jvvVZFfIDd8UyKgttqYmddkGBMgmQhUvFHNfqSFtwUjvPG0Q4uKfX1uhA5rsb59a9/zcqVq1i6pH93ygzDIKLqNIfjNIVV2qMaXrsZRYHGkIo/2rdzmNvt4tN3fJoXX3iRnTt39msMQ8GsKHx57RQW57v4ycZaNlQNPi96sgXJEiALMc4FYhq6bvRahueZAy1YTOBoqGTJ0qFJrzAMg1Bcozms0hRSietQkuFgeaGbM6d6mZPtpDjdzqpiL0sKPKTZzTSHVVrCKnFtbAbKpaWlZDgUPjpVZVttkHs21KAPMJfQZTVjUWB7rQTJQqTSEIxhN/Ueivz9QBuaAVeX9Vyn3MDg0UceJT0tjcsuu6xPYzjZpKQppNIc1rCYFWZlOllR6OGsaWmU57lZXuhheaGbdIeZ5rBGW0TttVJNdnY2//Kv/8Ijjz5K5cHKPo1lKNnMCt86t5hZWQ5+8HoVu+tDgz7mZAqSJUAWYpxri2iYewmOT5Z2O6vQweGD77J48eIBv56mG/ijGk2hOM1hDYfFzNxsJ6uKPawu9jLNZyfNbukSsJsUhQynhfI8N6uL05ie4SCk6jSF1DEXOCoorFq1CqVqLx9fnMOrRzr41db6AS+4cdskSBYiFd0waAr1fvcrqhn8vbKVVUWeXttK//Of/6SqqoqPfuxj3U4aqCfPYeE4zWEV1YCiNBtLCtycNc3L0gIPRel2vHZz5zEURSHdkTiHrSryUOC10RpJTA6oPdxlKi4q5uMf/zgPPPBbqmtqenlHhp7TYuI75xWT57Hx7X8e52BLZNDH9NrNaLo+4YNkCZCFGOfqAjHctp7/lF861E5ENSjTa5k5YwZuV/87vSUuZnH8cZ1Ml4VF+YmLyeICN/leG64+LLIBcFpNTPPZWTvVy6J8F06bmaaQSktERRtgOsNQO+OMlezatZNrSj1cW5bJ+opWHt078MUuEiQLkSwQ01D13hcX//NwOx1RrdfSbu/s28fLL7/MbbffjsOeukayP6oRiOtkuawsynOzutjLqiIvMzKdZDgtWPuwlsNlMzMry8maYi+lmQ4CcZ2mcJxoN8Fi2Zw53HDDDfzf//0fLa2tvR5/qPkcFv77gqm4bCa+8Y9jNIUHX4IyzW5B03V2TuAgWQJkIcaxcFwnGNOx9XBS1zF4pqKFudkO6g7uZemyZQN6rdawxrR0O2dO9TI3x0WWy9qni0l3TIpClsvK4vzEjMzUNBv+WGJmOhTXR7XRhi89nZkzS9mxcwe3Lc/l/Olp/G5nI/840j7gY0qQLERXreHe734ZnChN6bOzKL/7L/Z19fX8/ve/55Of/BRZmakD6ZimoxkGKwo9lOU4yXJZ+7Q4sDt2i4nidDtrir3Mz3GhGtAUiqf8+16+bBnnn38ev/j5zwkEgwN+zYHKdVv57wumEoxp/HZ7w5AcM81uQZ3AQbIEyEKMY4GY1uv6li1VAWoDcS4rcVFZWcnChQv6/Tr+qIbXYaIkwzEsLUddNjMzMp2snZrGgjwXbquJtohGczhOW0QdlSoYa9asZsOGjZhQ+OLqQsqyHfxqSz2BQQS3EiQL8b66QKzX9IrddSEOt0W5piyj2yYfwVCI++//FVdffRUzZ8xI+RzdMGiLaMzPceHsQ8e+/rCYFPI8NlYVeVhc4MFhNdEc1mg9ba3F+eedT3l5Ob/61a+IxmJDOoa+mJpu5/p52fzjcAd7GwefjwwTO0iWAFmIcawhEMNh6TlgXX+glSynBXfre8yePQunw9mv14hpOnHDYF6Oq0+drgbDbFLIcduYm2XnrGlpLCnwUJxuI66TWEBzImd5JGaX582bT0tzM3V1dVhMibJJ7VGNP+weXPF9CZKFgIiauPtl72UG9y8VLXjtZs4t8aXcruk6D/72t8yfX87qVau7PU5zWGVGhp0s9/A0C4FEnnKm08KSAg9Lch1M9dmIaon0i5YTX/SvuvpqsrOz+d3vfoemj3xA+cHyLLKdFn65pR5tiM6jaXYLqjHxgmQJkIUYp06Wd+tpNuRkabcPzPaxa+cOli7tX3qFcWLWZV62s885xkPFbFLwOSxMz3CyutjL6mIv5Xku0uxm2k4sEmwNq93m/Q3+9U2csXIlGzdtBGBWppPLZ/l4pqKVI4NsAHIySN5WE5AgWUxK/mjvd79qA3E2VQX4wCxftxMBf3nqKRSTiWuuuabb43REVTKdFkoyHAMfcD95bKbOc9cZU7zMzHAQ16ElonL5dTcTUXUeffSREe+257CY+NSyPA62RIakPvJJabaJFyRLgCzEOBWIaRi9lHc7Wdrt7AIr7713mPL58/v1Gq1RjeJ0G7ke22CHO2hOq4kct5X5eW7OmprGskIPJRkOdBKzyx1Rdchfc9WqVWzevAVVTRz7lkU5uGwmfrm1btAXNrfNjNWkSJAsJqW+3P16pqIFswJXzM5IuX3rtm3s27+PW2+9FXM3peKiqo5uQFm2a1jSw3qjKAoem5nidHtiQeAUL3Ny3Xx83ToO1TTz+N9eIBwf2YDynBIv5blOHtzRiH8Izz0TLUiWAFmIcao1ovZ4wj9Z2u3caekcOfAO8+bNxeHo+wxKMKbhMJuYmdm/lIyRYDYlSi5N89lZWeRlVbEHl9VMUyg+pOkXebm55OXlsXfvXiCxGvyWRbnsrAvx5tHBF96XIFlMRppu0NTL3a+QqvPCwTbOmpZGtit1WsQ///EPrrvuelzO1Oco3TBoj2qU57mHPO94oFw2M0Vpds6amc19X7mNQ5v/yeZNG2gKJRqTjEQlHwWFf12Rjz+q8cdBpoyd7mSQvKc+OOabQvVmbHxihBD9VheI91je7eUTpd2uLstgx/bt/eoopeoGYVWnPM+FZZjzjoeCy2pmcYGbaT47TaGhXdS3Zs1qNmzc0Pnz5bN9TPfZuX9b/ZDMkrhtZmxmCZLF5NGX5kYvHmwjpOrdlnarqq6mvaODefPmdXuMlkgi7zjTaRn0mIdDQU4WP/7O19n09MMo1e8wI8NO8wgtSi7NcAxZytjp0mwW/FGdQGx8zyJLgCzEOBSO64R6KO92amm3XGucY8ePM68f6RUtYZWyHCce28jmHQ+GSVGYmelkcb6LYFxPtLAdAosWLebokaO0tiXql1oUhU+vyKcxpA6qNvKpXFYJksXk0RKO97jgV8fgrwdaKMt2UJadenZ408aNrFq5stsguz2qkuEY2bzjgSgoKODb3/42v/zZffiPV7Ikz00wrg+qWk5ffWwIU8ZOZ1agMTjylTqGkgTIQoxDvZV321odoCYQ5+qyLHbu3El5eTk2a99Wb7dGNAq8NgrGQN7xQGS7bayY4sFuVWgKq4O+zWe32ViydCmbNm3qfGxBnotzS9J4Yl8TdYHBF92H94Pk7RMkf0+I7tT64z2Wd9tSFaDaH+eabtpKx9U4W7duZdWqVSm3R1Udw4C5OaOTd9xfs2bN4q677uL73/8+/sZqVhR6sJgUmofg/NWTU1PG3joWGNJje+1mavzxcZ1mMawB8r333sv8+fMpLy/nQx/6EJFIhMOHD7Ny5UpKS0u5+eabiZ2oBRiNRrn55pspLS1l5cqVHDlyZDiHJsS41tsCl/UVidJuZ071sn3bNpYt7Vt6RTiuYzXBrCwHyji4sHTHZTWzJN9DcZqNxlDXWqQDsWbNGjZt3NQlv/mTS3MxKQq/2lo32OF2clnNxDWD5tDQBN1CjDWhuEZENXpsbvR0RSvZJ85fqezatZuioiKys7OTtmm6QXsskXc8mCYgI23JkiX8y7/8C9/85jcJtrewtNBDnsc67HnJl8/2UeKzc/+2OiLq0L2OxaQQ03Q6huhO3mgYtk9PdXU19913H1u3bmXv3r1omsYjjzzCV7/6Vf793/+dgwcPkpGRwQMPPADAAw88QEZGBgcPHuTf//3f+epXvzpcQxNiXOutvNvxjhjbTpR283e0UVdXR1lZWZ+OG4gn8o57uniNF2aTwqwsJwvzXHTEtEGlLhQXF+F0uXjnnb2dj+W4rHxoQTYbqwJsrR262RePzUSNf3zfmhSiOx0RjZ5ufx1pi7KjLsgVczK6Xf+waeNGVq1OXfO4JaJSmukYs3nHPTnvvPO49tpr+frXv044GGButpM5WU5aIuqw3VWyKAp3LM+jIajyxL6moT22SaExOH6/7A/rVVBVVcLhMKqqEgqFKCgo4B//+Ac33HADAOvWrePpp58GYP369axbtw6AG264gVdeeWVcT80LMVx6K+92srTb5bMy2LFjBwsWLsRi6f1i0RJWmZ3lIM0+/i4sPcn12DhjigezSaElMrBblgoKV155JX/5y186S74BXDc3i0KPlV9uqSc+RLM8DosJf0wjJLnIYgJqCMVxmnu6+9WCzQyXlvpSbm9qbqaqqopFixYmbWuPqmQ5LUxNtw/VcEfcddddxxlnnMF//ud/EovFKEq3s6zQQ0TVh6WUJcCifDdnTfPy2DvN1A9hQOu1makJDG1loZE0bAHylClTuPPOO5k6dSoFBQWkp6ezbNkyfD5f58W6qKiI6upqIDHjXFxcDIDFYiE9PZ3m5qFZACPERNLTApdgXOelQ22cOy0dn8PC9m3bWbas9/SKtqhKjttKUdr4zDvujdtmZmmhh3y3lcaQijqAYHb+vHnk5uTy6muvdT5mMyv8y/I8qjpirK9oGbLxKiQ6fwkxkai6QXNI7Tb/uCOq8crhds6f7sPnSP1FfdOmTSxbvgyrpeuaipN5x2XjJO+4J5/4xCcoKCjgBz/4AZqm4XNYWDHFi9Nqpik0PHnJn1qaB8BvttcP2THNJgVV02mPjM8v+8M2VdTa2sr69es5fPgwPp+PG2+8keeff37Qx73//vu5//77AWhsbKSxcXA1/FpbWwc9pslG3rP+Ger360BdGLNJoTWSfBF49UgHHi3ERVN8HD5ymHA4TG5uXmcFhlRimkFUM5iZ66CpKTSkYx2o4fqMZQGqWeVgQxy7mR4XCqVy0cUX88c//oGyOXNwe9wAzPbAOfkKz+0+xvJMnfRuLuz9oWoG7wQ6sOf1LRdc/ib7R96v/huK96w9qtHeHsUUTV0d56WDbaTpIS4szEh5zjIM2L17F9ddd32X7bph0BLRWZRjx98aY/AVygdvsO/Xxz72MX75y1/yi1/8gptvvhlFUZhiNohrcQ7WqvgcpiEtwWkDPjTLzt8PNLD5oJlZ2UNT/SMU0zlQFWSmr/fJl7H2dzlsAfLLL7/M9OnTycnJARK3Dd566y3a2tpQVRWLxUJVVRVTpkwBEjPOx48fp6ioCFVVaW9vJysreQXr7bffzu233w7AokWLOo8/GENxjMlG3rP+Gar3KxzXsfn9ZLuS/3R1DP56pJnsrAwWleTz4ksvMmv2bLIyU9cRhcSFpSmssrbAQ8YYy9kbrs9YTg6UxDR21wUB+lXKLsOXwcIFC3n1tdf46Ec+0vn4x1a6+de/HuKJQ3HuXDs0424Kx3H5vH0en/xN9o+8X/032PesoyVMpi+WcnZY1Q2eOdZEUV4W5VPzUu7/zr59WCwWyubM6fJ4QyjOsgIHJb6xVdJtsO/XV7/6Vb7yla/w4osv8tGPfhSA/Dyo9cfY1xDCYTMPaQOUaxf7eP64ym8rwvzsA/lYhmAmPk036IhpZGal9Vja76Sx9Hc5bCkWU6dOZdOmTYRCIQzD4JVXXmHevHmcd955PPHEEwA89NBDXH311QBcddVVPPTQQwA88cQTnH/++eN6Fb0Qw6GnHLRt1UFqAu+XRupLekVLRGWGzz7mguPh5rElGotoRmJVfX9ceuml7N+3j6PHjnU+VuS1cd3cLF4+3M47jeEhGaNZUWiSahZigjAMg/pAHLc19Re+jcf9NIVUru6mMQjAxo0bWbN6TZfH2qIq2eM877g7LpeL733ve7zyyis8++yznY8XeG2sKPKg6gbtQ5iX7LAo3LYsnyNtUZ59t21IjplIs4C2yPhLGRu2AHnlypXccMMNLF26lAULFqDrOrfffjs//OEP+fGPf0xpaSnNzc188pOfBOCTn/wkzc3NlJaW8uMf/5i77757uIYmxLjVGIzj7Ka82/qKls7SbvUNDfj9fmbOLO32WP6ohs8+9gvpDxeX1czifDcRzSDajxXiDoeDK6+8kscff7zL4pMPLcgm22nh/7bUoQ1BjqDHaqamIyaLlcWEEI7rRFQdazcL9N467sfnMHPGFE/K7f6AnwMHDrBs+bLOxzTdQNcnRt5xdzIyMvj+97/PH/7wBzZseL+jZ5rdwvIpHuxm05Au3ls71cPifBcP72qgfYhKtDksCvWB8VeZZ1irWHznO9+hoqKCvXv38vvf/x673c6MGTPYvHkzBw8e5PHHH8duT3zrczgcPP744xw8eJDNmzczY8aM4RyaEOOOpifSIVLdUjveEWNrbZDLZ/uwmBS2b9/G4iWLu71oGIZBRDMoy3ZO2AtLX3jtiSC5I6r1q73rGStXYug6W7du6XzMaTHxyWW5HGyJ8MLBtkGPzWpWiGgGfqlmISaA9qja7V1h1TDYWhNgeaEHczfP2bJlC+Xl5Tgd73fWa4uqlGTYx1W944EoLCzkO9/5Dj/96U/Zu/f9UpN2i4mF+W5MijJkHTgVFP51eT6hmM7vdzYMyTHdNhP1QZX4CLTQHkoT+1MlxATSU3m3v55S2s3AYNu27SxbtryHY+nkuS24xlEr6eHic1hYlO+iLaL1uSC/SVG44cYbeGb9M0Qikc7Hzy1JozzXyYM7G4cksLUojOs6okKcVB9Qu737VdEUJhDTu509NjDYuHETa9a8X/vYMAwMA/LcE7PyzulmzZrFV77yFf7rv/6Lo0ePdj7usJhYnO8mrhuE40MTgJb47FxVlsHfK9s42BrpfYdemBQFwzDGXTULCZCFGCe6K+8WUnVePFHaLcNhoaamlmg0SklJSbfHimg6RRMwZ2+gst025uU6+9W1anrJdGbNns0LL77Y+ZiCwh0r8glENR7eNbgKO5CoI1rtH791RIUAiGs6LWEVZzczvVurA5iApQXulNsPHz6CpqmUlr6fMhaI6eR5rEO6SG2sW7ZsGbfddhvf+MY3ulTwctnMLCnwEFL1fqWL9eSjC3Pw2s38cksdBoM//zgtJmrHWZrF5PlkCTHO1QVS1w/dXhMkohpccqKw/vbt21i6dEm3qRMRVcdtNZNul9njUxV67czOctIc7nud0auvvpqNGzbQcMrFamaGgw/M9vG3A6281za42RezSUEb5+1ahfDHEkFbdykWm6sDzMt1dluxZePGjaxatQrllBZ8YU2neBJ+yb/gggu4+uqr+cY3voHf/35BO6/dzJKC/qeLdcdjM3Prkhz2NoR57cjgC+e5rCYaQ/EhGdtIkQBZiHEgHNcTJd5StIDeXB3AYzUxN9eJgcH27TtYunRZiqMkBOIa0zPsUiUmhanpNkp8dpr6GCT70tM5/4Lz+ctf/tLl8VsW5eKxm/nF5sHPvljNCvUBSbMQ41dzKE532VxNoTjvtUa7Ta+IRCLs2rWTM85Y2flYOK6TbjdPuK6ffXXDDTewbNkyvv3tbxOLvT8rezJdrD2iDagZ0ukunumjNNPBL7fWUe0f3OyvoihgQNs4aoAkAbIQ40BHVIUU8ayBwZbqAEsL3VgUhePHqzB0nalTi1MeR9MNLIpClsuacvtkpygKMzIdFHhttIT7Nmt73nnnUVtby/6K/Z2PpdnNfHxxYvbloZ2NgwqS3VYzdYFYn1M/hBhLTpZ3c3VT3m1LTQCAM6Z4U27fsXMHM2eW4ktP73wsENeYPsZqHo+0T33qU/h8Pn73u991eTyRLuaipR/pYt0xKwp3rilEN+CrLx4ddJDsspqo8Y+fL/sSIAsxDjQE4zhTlEc61BKlNaKyojAx+7J92zaWLlva5VbkqdqiKsXptiHtwDTRmBSFOVlOslwWWvow22G1WLn++ut48smnULX3g+rLZvm4tNTHI3ubeXhn04CDZLNJQdMZspJLQoykUFwnpuvdnnO2VAfIdlmY1k2ntQ0bNnZZnBfXDGxm06Sr3X46k8nE5z//eV577TV2797dZVuB18ac7ES62GDXL5T47Nx90VSiusFXXxpckOy0mGgJx4kMUZ70cJMAWYgxTtMNmkKpy7ttPTH7snyKJ5FesWN7t+kVJ1d953smX95ef5lNCnNznHjsJtr6UGO0vLwcn8/H66+/1vmYCYXPrcrn0lIff97bNKgg2W5WqPNHB7SvEKOpLaJ283Ud4rrB9togZ0zxpPxSX1dXR3NzE/Pmze98rCOmUeKz96kr20SXlpbG5z73Oe655x5CoVCXbcXpdqZn2GkO9X1NRXdm+Bz88KKpRLVEkFwzwMV2iqKAooybNAsJkIUY4/wxDYzU5d02VweYlekgw2HhyJEj2Kw2pkwpTHmcybjqezCsZhMLct1YTSb8vczeKijccP31vPjCi/gD7y9oGaogebzWERWirofuee80hIioBiu6yT/euGkjK1euwmxKnLN0w8DAINctKWInrVy5kkWLFvGb3/wmaduMDAdF6Taah6CL3alB8ldeHHiQ7LGaqOoYH1/25UopxBjX2k15t46oxv7GcOfFZdu27T2mV0Q0g6I0mT3uD7vFxKJ8Fwa9t6TOz89nxRln8Le//a3L40MRJJ+sIyrVLMR4EtN02qMq9m66522uDmAxwZL85PJuqqry9tubWbVqVedjHVGNKV479gneGKS//vVf/5WtW7eyZcuWLo8risKsLCe5bivNQzBrO8Pn4O4LBxckOywm2qN6r+fTsUA+ZUKMcd2Wd6sNYgArpnjQDYOdO3awdMnSlMeIqDpem4k0Ke3Wby6rmcUFiZbUveXOXXbZZezevYdjx493eXwogmSnxUTNIBfJCDGS/FENjJ7Luy3Mc6fshLd3717y8/PJy83tfCyuG0xJmxyNQfrD5XLxpS99iZ/+9KddSr9B4sv13GwXGQ4zrUPQqGNmxokgWdX5yotHqR1AhR2TAq3jIM1CAmQhxrCeyrttqQ7gtZuZneXg0KGDuN1u8vPzUx4ncCJvT0q7DYzHlmhJ7Y9pPS56cTmdXHHFFTzxxONJAfBgg+TxWEdUTG6NQRV7inMXQG0gTlVHrNvybhs2buiyOC8Y08h0WrqtlTzZLVq0iLVr1/KLX/wiaZvZpDA/14XLqtDehzUVvZmZ4eDui6YRVXW+/OKRfgfJiTSLsf9lXwJkIcaw7sq76RhsrQmwvMCNWVHYtm07S5amnj1WdQOL2SSl3QbJ57AwLd1OWy+zMKtXryYWi7Nt2/akbYMJkk/WER0PMy9CGIZBQyie8u4XJL7gQ2KB8ela21o5euQoixYt7nwsrOpMnYSNQfrj1ltv5d133+WNN95I2mY1m1iY58ZiMhGMDc1M8g8GGCTbLSYCMW1IxjGcJEAWYgzrrrxbZVOE9qjGiiIPmq6za9culnYTIHdENaaly6rvoTDVZ8ekJEpNdcekKNxwww08/fTTRKLJi1FOBsmXDCBIdllN1I6jOqJi8grEdFTd6Pa8s6U6QKHHSpE3OWVi06ZNLFm6FLstsS2m6TgsUtqtNw6HgzvvvJOf//zntLa2Jm23W0wsyHMRUo0haV9fekqQ/JUXj1DXjyDZpNCnMpqjSQJkIcYoTTdo7qa828ni+ssKPBw6dAifz0duTk7S8xKl3QzyPDJ7PBRsZhOzspy9ln4rnTmT0pkzefmll1JuN6Hw+QEEyeOtjqiYvNqjKko3n+mIarCrPsAZRcmzx7phsGnjJtasWdP5WEdUpyTDnrKSj+hq7ty5XHrppfz0pz9NWd7NYzMzLd3W652wvirNcPCDC6cRPjGT3Ncg2Wszc7w9OugSdMNJAmQhxih/TMMgdXm3LdUByrIdpNvN7Nixg8WLF3d7jDyPLeUiGDEweR4raXZzr6uwr7nmGt586y12nVbE/6SBBMkn64i2hmUWWYxtTUEVlyV1vvDu+gAxLXV6xbvvvovT6aS4uAhITBSYFMiRFLE++/CHP0x9fT0vdfMFvS93wvqjNNPB3f0Mkm1mExFNJxgfu1/25aopxBjVEopjThEct0VUDjRH3q9esXNntwFyVDMoSpdV30PJpCjMznISiOs9zn74fD4+/elP89ijj/LWhg2pjzWAINljNVE9Dha4iMlL0w1aIyp2S/fVK+xmhQW5yeXdNm7YwKrVqzvLVXbENIrTbVi7WewnktlsNr785S/zm9/8hoaGhuTtfbwT1h+nBslfealvQbJZUWgMjt1zmXzihBij6gKpF7hsqw0CcEahh0OHDpKeltalFNJJ4bhOmt1Mml3y9oZausPCFK+t1/bPU4uL+dznP8+LL7zA8y+8kDL4PT1I/sOuph6P6bCY6BgndUTF5BQ88dlMdffLwGBLdYDFBe6k+sjBUIh9+/ezYsWKxHMNA1WHAo98ye+vGTNmcN1113Hvvfei68mztHkeK15b73fC+qM008EPLpxKKKbzv2/X9vp8j9VMTUd8zKZZSIAsxBgUimtEVKPb8m4+h5mZWY4e0ysC8URpNzE8pmc40IzEbFlP8nJz+fcvfpEd27fzxBNPpFwcczJIPq8kjT/uaaIx1PPsizIOFriIySvReTL138Xx9hj1QZUVhcnpFVu2bGH+vHm4XS4AgnGdHJcFl5R2G5Abb7yRcDic1LwIEl9e5mQ7CcR6vhPWX7MynVwxJ4MdtUHaeungZzUrRHUj0S12DJIAWYgxyB/VUpZ304wT5d0KPWDArp27UpZ3i2uJ4DrTKXl7w8VhMTEr00FrH9q4+tLT+fwXvkB1dQ0P/e53qGryPiYUPrIosdDy9aMdPR5P0izEWNYYVHF2s+7h5ALj09tLGxid6RUnRVSdYintNmBms5k777yTP/zhD1RVVSVtT3dYKOzDnbD+OrckHR1446i/1+daFGgMjs01FRIgCzEGdVferaIpQiCms6LQw+HDh3G53SnTKzpONAaR0m7Dq8CbWADZl6oSLqeTT3/608TVOL/85S+JRCJJzyny2ijNdPDq4Z4DZLslUct0rNcRFZOPbhi0RdVuFwZvqQ5Q4rOT5+765b26qppIJMLs2bOBRHDssprxOWT2eDCKior4yEc+wj333JMy1aKvd8L6o8RnZ1q6jVePtPf6XK/NTLU/PiRl54aaBMhCjDE9lXfbWh3ABCwtdLNjxw6WLl2S9BzdMDAwyHXL7PFwM5sUynJcdPRxBsZmtfLJT36KzKxM/vd//xd/IHmG5dySNCpbIlT10lZaUaApJGkWYmwJxnR0I3X+cTCus7c+lLgDdprde3azZMmSzv0CcY3pGdL9cyhceeWV2Gw2Hn/88aRtTquJ0kzHkJV9O+nc6Wm80ximoZfZYbNJQdP0Pp9DR5IEyEKMMYGYhmF0U96tJsC8XCduq6nb6hX+qEah14ZdSruNiEynhTy3NdH1sA/MJhMf+tCHKJtbxr333ktTc3OX7WdPSwPgtV5mX7w2M9UdY7uOqJh8Onqof7yzLohqkLK99O7deygvLwcSkwQWRZHun0PEZDLxxS9+kSeffJLDhw8nbU9cL5Qhra9+9rR0oPd0MUjkItf3s131SJArqBBjjD+WOv+4ORznYEuE5YUejhw5gsPhoCC/IOl5Md1gilfy9kbSzEwHMc3o821KBYUrr7iSs88+h5/85CdU19R0bst1WynPdfLq4Y4eS77ZzCYiqkEgNnbriIrJpzncQ/5xdQCXxcS8HGeXx1vbWmlrbWX6jBkAtEVVpvrsWCRFbMjk5eXxyU9+kh/96EdJqRZmU2LB3lAulpvitTE7y8GrR3oPkN1WM/WB2JCmeQwFCZCFGGO6K7C/tTpR3m3FFA87d+5gyZLk9IpQXMPnMOO1S97eSHLZzMzIdNDWz9uE555zDtdccw0/+9nPOHjoUOfj50xL53hHjMNtya2qT2UxMabriIrJRTcMWsKp849PlndbWuhOCnz37NnDvPnzMJtMJ7p/Qp5bSrsNtYsvvhiXy8XLL7+ctC3TaSHbZT1RgWRonFOSxsE+pIuZTQqqDv4x9mVfAmQhxpCTBfYdKQrsb60JkOW0MN1n7za9IhgzKPE5RmCk4nRFaXasJoWY1r+T/PJly1h3yy385je/Yc/ePQCcNc2LCXpdrOexmakZowtcxOQTjOkY3eQfH2qN0hxWU6ZX7NmzlwULFgAQiOnke2wp12CIwVEUhU984hP84Q9/IBaLJW0rzXQQ1YwhO5+c08d0MQCbGVqjEiALIboROtF28/SFKapusL02yIopHo4dO4bVYqWwsGt6RVwzcFgUMpzSGGQ0WEwKc7IdAyqZVFZWxh133MGf//xnNm/ZjM9hYUmBm9eOtPeYZmE5EZAH4hIgi9EXiHWfh7+1OlHe7fT20pFIhMPvvcfcsrmJnzVdun8Oo3nz5jFjxoyUtZHdNjPTfDZah2jBXrarb+likEg70yXFQgjRncRCr+STxP7GMMG4zvJCDzt37GDxksWdrVg7941pTPPZU87eiJGR7bKS4bAQGEAu37SpU/n0p/+Np576C3E1zrkladQHVSqaksvBncpqUmgKjb0V4GLyaQypOFKUp4REe+nSTAeZjq5f4PdX7Gf6jBk4HA7p/jlCbr31Vh577DGCwWDStqnp9s4v3kPh3JK+pYuNRRIgCzGGdLfAZXNNALMCi/Nd7NixgyWLu+Yfn7wlliOl3UaVoijMynISVvUB3aYsmjKFKYWF7Nq1m9VTvVhN9FpL1GMz0xBSJc1CjCrdMGgNpy5P2RHV2N8YTtk9b/fuPSxYkKheEYxrTJcUsWE3bdo0VqxYkbLsm9VsYnaWg/bI0ATIZ07tW7rYWCQBshBjRE8LXLZUByjPddFSX4PJbGZK0ZQu26W029jhtZuZmm4fcF3R1WvWsHHDBjxWMyumeHj9iB+th+D35AKX4Bhb4CIml1BcR9NT5x9vrw1iAGcUdQ2QNV1n/759LFiw4ES5QoV0aQwyIj72sY/x97//nebTykxCopJOmsM0JI2IfA4LS/uQLjYWydVUiDEiFE+9wKUxFOdIW5QVUzzs2LGDxYuT0yviOtIYZAxJpLok8sL7a9GihVTX1NDY1Mi5Jem0RlT21Id63MekGLT3sQ6zEMPBH1XpLrtrc7WfNLuZ2VldZ4ffe+8QvowMMnwZhFWdTKcFq1nCkpGQm5vLRRddxJ/+9KekbYqSKPsWVPUhqbN+Th/TxcYa+SQKMUb4uwlwttacWNxyonve6eXdDMNAUZDSbmOIzWyiNMtB2wCCVqvFyhlnrGDDho2cMcWDw6L0WkvUYVF67VglxHBq7ib/WDMMttYEWV7oxnxaBL13714WLkxUrwjFDfI8kns8kj74wQ/yxhtvUF1dnbQtzW6hKM3W79KVqZxMF+tLNYuxRAJkIcaI7i4wm6sC5LgsmAONGLpOcXFRl+2heGLmRYrqjy35Hhtem3lAtynXrF7D229vwqIYrC7y8uaxDuI9rPC2mxXawhrxIVpYI0R/6IZBczfpYZXNETqiGitSlnfbQ3l5IkBGMUiXxXkjKi0tjWuvvZaHH3445fYSnwODRBWlwTiZLvZaL+liY40EyEKMAUY3F5i4brCjLsgZUzzs3LmTJUuWJKVXhDWDPEmvGHNMisLcHBdR3eh3C9f8/Hxyc3LZu2cP55akEYjpbDtxJyEVRVFQlLFXaF9MDqG4jm4k8uFPt7k6gAIsLegaINc3NBCLxSkuLkqUqDSbcNnkLthIu+aaa9izZw+VlZVJ2xwWE6UZjiFJ3+pruthYIgGyEGNAKK6jGUbSBeadhhAR1WD5ifzjJUuXJu2rAGmysGVM8trNLC1wE4zr/Q6S16xdw4aNG1ha6MFjM/WaZmFRoDUsaRZi5AWiGt1NDG6pDlCW7SD9tBSwPbt3U15ejoJCMK6R55Ev+aPB6XTyoQ99iN/+9rcptxd4bTgt5gGVrjxVX9PFxhIJkIUYA/xRDYzUsy8WE+TpbaiqytSpxV22xzQdu1nBZZUAeaxKs1tYVtj/IHnx4iUcPXKUjrZWzpyaxqYqf4/7u6xm6gMSIIuR1xSK40zR/bMlolLZEmFlkTdp2549ezq756m6QaZTAuTRctlll1FXV8eOHTuStplNCgvzXRgGg6pq4bCYWF3k5a1e0sXGEgmQhRgDWsLxlO2lt1QHWJDrZt+e3SnTK0JxnXyZeRnzBhIk26xWlq9YzsaNGzm3JI2IarCpqvs0C6tZIaoZhOLSNESMHKOH8pTddc/zB/zU1NYye/YsdMPApCikySLjUWOxWFi3bh0PPvhgyqoVLquZJYVu9EEGyeeUpOGP6WzvIV1sLJEAWYhRlsg/1pIahNQF4hzviHFGUSL/ePHixUn7arpBpksC5PEgzW7pd7rFmjVr2bRpE/OzEx3IelsFbnDiboQQIyQU11FTpIdB4gt+psPCzAx7l8ffeWcfc2bPxmqxEo7rZLksKfcXI+fss89G0zTefPPNlNuHIkhediJd7LWj4yPNQgJkIUZZOK4T1/SkC8TJ8m4l5gDRaJSSkpIu23XDQDEpeGVhy7iR7uhfkDylsBCfz8eBiv2cXeJlS02gx1xAp1mhScq9iRHUXXqYqhtsrw2yosiTdOdr7549lJ9IrwhrhtRwHwNMJhOf+MQn+N3vfoempT7HuKxmlhQMPEi2mhTOnJrGhuM9p4uNFRIgCzHKAnGdVBX2N1cHKPBYqancm7I5SDiuk+OUmZfxpr9B8tq1a3hrwwbOLUlH1eGtY/5un+u0mmgKS9tpMXJawqnzj/c1hgnG9aT20nE1TsWBA8yfPy/xgJG4uyJG39KlS8nJyeGFF17o9jku2+CC5JPpYluqx36ahQTIQoyyplAc+2lBblQz2FkXZPkp5d1OF1ENcj22kRqmGEL9CZKXLFnKe4cOkWeJkO+x9LgK3KQo6AaDXnEuRF8YhkFTKHX+8ebqABYFlhS4uzz+7ruVTCksxOvxEtN0nFYTTquEImOBoih84hOf4I9//CORSPdd704GydoAguQFeS4yHBZeHQdNQ+RTKcQoMgyDlpCK67QLxJ76IDHNYJYtTDgcTkqvgES+qXTPG79OBsmBuNZjkOyw21myZAlvb3qbc0rS2VkXpC3SfV1SRYH2iATIYvh1V54SEili5Xku3Kef206pXhGM6RR4Jb1iLJk9ezZlZWWsX7++x+e5bIkSlv0Nks2KwtklXjZXBwjGx3aahQTIQoyisKoT05Pzj7fUBLCZIVZdweJFizCdloIRVXXcNlPKmRsxfqQ7LCwr8PQaJK9Zu5YNGzdw9lQvBvD60e7TLFwWE/WB2DCMVoiuAjENg+TguCEY50hblOWnpVcYGOzds4cFCxcCiXUUGQ5Jrxhrbr31Vp588kn8/u7PMzDwIPmcknTiOmw8PrYX68nVVYhRFIrpKCkWuGytDrAoz8OeXamrVyTKu0l6xUTQlyB5anExbreHaP0RpqXberw96bCY6IhpxKTttBhmzaE4DnPy+WtXXRCApYVd0yuOH6/CZreRl5uLphuYTIrcBRuDioqKWLt2LY8++mivzx1IkDw320Gu28KrhyVAFkJ0oymkYjvtAlPlj1HtjzPHGcEf8DNj5syk/TTDIMMpF5aJIpFu4SHYQ5C8ds0aNrz1FudOT2NfY5j6HqpVKIYi5d7EsOquPCXA7oYQXpuJEl/X8m579uxh4YLE7HFY1clxWZLujomx4SMf+QgvvPACTU1NvT63v0GygsI5Jelsr+05XWy0SYAsxChqCceTFqicLK5va6xk8eLFSRcQTU/k/HmkvNuE4nNYWFLgIRjTiGvJVSiWL1/Ou5Xvsiwz8Xl5vYfFelYztITH7oVHjH/dlaeExBqK8lwXptPSL062lwaIqDo5brkLNlZlZ2dz6aWX8vvf/75Pz+9vkHxuSRo68GYPVXlGmwTIQoyScFwnohpYTs8/rg5QlGbjvf17UlavCMVl5mWi8jkslGY56YglB7cOh4OFCxdxdN92Zmc5eqxm4bSYaQzGU3bFEmIodFeesj4Ypy6gsii/a3pFa1srbW1tTJ8xo/MxSa8Y22666SY2btzIsWPH+vT8k0Gy2ocgeUaGnaI0W6/Nj0aTBMhCjJJQXENRugYwMc1gT0OQea4Y7e3tzJxZmrRfVNfJlZmXCSvLaaG7sHbt2jVs2LCRc6Z5OdQa4XhH6sV4VrNCRNMJj/FV4mL8auom/3hPfQiA8jxX18f37GHe/HmYTSaiqo7XbpZFxmOc1+vlxhtv5N577+11wd5JJ4PksKr3WI9dQeHckjT2NIRpCo3N5kby6RRilLSEVaynzR6/2xwmpoGjuZJFKapXACiGzLxMZC6bGbfVTDRFLnJJSQkWi4VitRGgx9kXBYUOyUMWw8AwDJpDasr84z0NITw2EzMyTs8/3tuZfxxUZZHxeHHdddcxb948PvvZz3L48OE+7eO2mcn3WAnGev6Cfm5JOgCvj9HW0xIgCzFKGoNxnJauge6ehsTq79bDFSmrV0RUHY/djF1mXia0ojRb4hb2aRQU1q5dyzvbNrEg18mrRzowuplvdpgVGsfozIwY38JxHTVFeUqAPXXJ+ceRSITD771HWVkZALqRSCcSY5/ZbOa2227jYx/7GF/96ld58803+7RfvtdONMVailMVpdkozew5XWw0yVVWiFEQUXWimoH1tFuUu+vCFFnC+NtbmTVrVtJ+wbhGgcy8THiZTivd3Z1csWIF+/bvZ3WehaqOGIdaoimf57SaaA6raLrkIYuhFYzrGCnKUzaF4tQE4iw8Lb1if8V+ps+YgcPhQNMT6y7cNgk/xpMLLriA73//+/zqV7/i4YcfRtd7nh1Ot5uxmOj1/HPOtDTebY5QFxx7tdvlEyrEKAjGtKQFVKpusL8pRLb/MIsWLcRsSv7zNAyFdJl5mfCcVhNpdlPKkm9ul4vy8nJs9RWYFbqtiWxSFHTdIBiXNAsxtJpCcewp8o93NyTyjxecFiDv3r2HBQsS1StkkfH4NWvWLO677z527drFd7/7XUKhULfPNZsU8jy2Xs8/Z5ekAfDWsbE3iywBshCjoC2SnH9c2RIhohpQ/x4LFy5K2kfTEzPOHpl5mRSK0uwp0ywgURN555a3WZLv4rUjHd3ONpsUhbawBMhi6JzMP3ZZU9Q/rgvhtpqYkeHofEzTdfbv29fZXjqm6+S6pb30eJWRkcHdd99NVlYWX/jCF6iqqur2uXkea8qSlV2e47YyL8fJW8cCQz3UQZMrrRCjoDGkJtU/3l0XhHiMSEsdpaXJ1StC8cSFRZGZl0khw2lBMYyUpdpmls4Ew2CepY3GkMp7bZGUx3BbTdSPwVuXYvwKqzqx7vKP64PMz3VhPuUc9d57h/BlZJDhywDAkEXG457VauWzn/0s11xzDXfeeSdbtmxJ+bw0uxmz2dRrmsW5Jekc74hR7R9b5yoJkIUYYTFNJxzTsJm7/vntaQiRE61jxvRp2G3JecZRTSfbJekVk4XdYiLDaSWcIs1CQWH1mjWED+/CZoZt1alnX+wWE4GolrIihhADEYzpKCnyj5vDcar9yfnHe/fuZeHCxOxxRNVJd1iSzn1ifLr88sv5xje+wb333svjjz+e9GXepChM8Vrx91IT+axpXgDeaew+ZWM0yKdUiBEWiOkYp3WYUg2DdxpCZIWqO1d6n8owDFBk5mWyKfSmDpABzjhjBRX79rIww8LexnC31SwMINCHzlZC9EVzSMWWsv5xGKBLgGxgsHv3HsrLEwFyMK6R55H0iomkvLyc++67jzfeeIMf/vCHRCJd72bluK2ovawTznBYuO+yEi6akT6MI+0/CZCFGGHtkTinV2l7ryVCWDXQG49SVjY3aZ+IauCTmZdJx+e0AErKNAuvx8u8uXPJbKukNaRS7U9d0s1mVmgKSdtpMTSaQvGk9DBIpFc4LQozMt/PP66vbyAej1NcXAQkvqxJebeJJzs7mx/96EeYTCa+9KUvUV9f37nNazNjNym95iIXeGwojK30wWG72h44cIDFixd3/ktLS+MnP/kJLS0tXHTRRcyaNYuLLrqI1tZWIDFD9rnPfY7S0lIWLlzI9u3bh2toQoyqxmBygf3d9SEI+7FqEYqKipL2Cas6ebKwZdKxmU1kuyyEulmst3rNGtoqd4AB22uCKZ/jskrbaTE0wnGd+IkybafbVR+iPNeN5ZT847179lBeXo6Cgqob2Ewm3CmCazH+2e12vvzlL3PBBRfwhS98gcrKSgAURaEwzUpgHFbTGbZP6pw5c9i5cyc7d+5k27ZtuFwurr32Wu6++24uuOACKisrueCCC7j77rsBeO6556isrKSyspL777+fO+64Y7iGJsSoiWs6gbiW1OhjT0OIjGAV5XPLUpY/MjBk5mWSKvTauk2zmD17NiYtTrrWxvba1HnIFpOSyHuXttNikFKVpwRojahUdcRYkOfs8vjuPbs7q1eETqRXyCLjiUtRFK677jo+9rGP8eCDD3Y+nuO2oY3DL+gj8lXulVdeYebMmUybNo3169ezbt06ANatW8fTTz8NwPr167nllltQFIVVq1bR1tZGbW3tSAxPiBETiOlw2gIXzTB4pz6EL1DNnLI5SfuouoHVZEpZVklMfOmORM1YPcUFxqQorFy1kgz/cXbWBVG7XS0ubafF4DWHVewp0rx21yUWVy3Mc3c+5g/4qa2tY/bsRMOjmAZZLrkLNhlceOGFHDlypLM1tdtqwmE2EdPG15f0EZmSeuSRR/jQhz4EQH19PQUFBQDk5+d35qpUV1dTXFzcuU9RURHV1dWdzz3p/vvv5/777wegsbGRxsbGQY3tZIqH6Dt5z/rn1PfruD9OwB/HHHt/sV1VRwxHPIA73MSUKUW0tnV9fztiOrlOC01NqTumTUTyGevKEotS7ddT1sCeMWMm23bvw6OG2Hmkjpmn5ICeFI3rHIj5MWfZR2K4Y558vvqvtbWVg5EwDouCGu76JX9/VRNTLBGyTRFaT5Qc3Lt3L/PmziUQCGAYBh1RnYg7SmNgcswgT/bP2OWXX87TTz/NRz/6UQBcapyjLXF8jtQLzQMxHWs8SGPj2OkUO+wBciwW45lnnuEHP/hB0jZFUfp9u+X222/n9ttvB2DRokXk5OQMeoxDcYzJRt6z/jn5fh2N+cm3geOUFIvX6lpoavdTYjEzberUpH21kMqsAjeZzsmVYiGfsfeZPSo7a4NkpCjz5/P5QI3S5A/yToeF5TMykp6Tbhi0RTQys9JS1q+djOTz1T8RVcdpcaQsNbmjtYX8nCyyM9//7B2oOMCChQvJ8GUQjuv4fCYKT5lhngwm82fs6quv5tZbb+WWW24hKysLV7pGc5WfjG7uIliiGuaoeUy9Z8N+z/a5555j6dKl5OXlAZCXl9eZOlFbW0tubi4AU6ZM4fjx4537VVVVMWXKlOEenhAjRtUTsyint2jdXR8kzV/FwvLU5d0UJVFwXUxe6XYzFhMpC+4rKMyeVUpB+Hi3ecgmJVEJQ8q9iYEKxg0UJfnz1xZROdoe69JeWlVVKg4cYP68eQCEVJ08z+T6gj/Zeb1ezjvvPJ555hkA3DYzHpt5XNVkH/YA+c9//nNnegXAVVddxUMPPQTAQw89xNVXX935+MMPP4xhGGzatIn09PSk9AohxrNATENRjC53TXQM9jSESfNXM3ducnm3sKqT6bSkXDUuJg+zSSHPYyPYzUrwmTNLcbYc4UBzpNtcY5Oi0BaRAFkMTHtUx2ZKDhn2Npyof5z//uzw4SOHyc/Lw+PxAIkv+ul2CZAnm2uuuYbnnnuOcDjxGSlKsxEYR4uFhzVADgaDvPTSS1x33XWdj91111289NJLzJo1i5dffpm77roLSOSrzJgxg9LSUm677TZ+8YtfDOfQhBhx/qiWVOfxWFsMfzCC0VbLrNJZSfuEVUPKuwkA8jzWbmuJTp1aDP4miIbYWddduTcTdYGx1cpVjB9NYbVLathJu+qD2M0Ks0/Jfd+3b39nw6O4ZuCwmFLWThYT25QpUygvL+ell14CIMNpoZeu02PKsH6lc7vdNDc3d3ksKyuLV155Jem5iqLw85//fDiHI8SoagzFkypR7K4PQWsVM0um4nAkL64yMEjrZlGDmFzS7GYsZhOabiTlEVssFhaXz6W65Sjbaws5e1pa0v52i4mmUJyoqieVGRSiJ+G4TkwzsKbsoBdiXo6ry12uiooKrj8xMRaMa0xJs0l5t0nq+uuv50c/+hFXXHEFLquZdLuJiKqn/LI11oz9EQoxAai6QXtES8o/3lMfxN1RxZIFyekVMU3HYTbhskqALBIpEoVeK/5u8ogXLCgnO3iMbTWBbttOo0jbadF/wW4+M20RlSNtURblv59/7A/4aWpqomT6dCBx7st0yl2wyWrevHn4fD42bNgAQFGafdykWUiALMQICMY0MLrmHxsY7GkI4e6o6rwdeapQXCffIxcW8b4ctxW1m9h3/vxyaD5Goz9CVUfqttN2k4nGoLSdFv3TEIpjSzF7/E5jIre0/JQFegcqDjBrVikWsxndMDApCl5ZZDxpKYrC9ddfz5NPPgmAz2kBwxgXnT0lQBZiBPhjGqe3mT/eHqOtrR1bPMDUqdOS9lF1g0wprC9O4bWZcZiVlLnIaV4vJVPyoaWabTWpq1m4rCYaQ9J2WvSdYRg0hVRc1lTpFUFsZoU5We930Ntf8X7+cTiuk+mSRcaT3dq1a2lpaWH//v04LCYyndZuu4OOJRIgCzECmoIqLkvXWZQ9DSFoOsb8sjLMp60O75x5scnMi3ifoigUptkIdFPNYsWSRXjbj7Cjm4V6ZpNCXNcJjZNbnGL0BeM6qpY4H51uV12IeTlOrCcCYAODiv0VnRV5wposMhZgMpm49tprO2eRC7zWcXEOkgBZiGGm6QatERWH5fT84xCO9mMsX5iivFtcJ8tlkaYOIkm2y4rWzQxw+fz5OFuPsKs2QLyb5eIKCu1RSbMQfdMeUVPWP+6Iahxui3apf1xTU4vVaiUn+0SzBwNJrxAAXHzxxezevZu6ujoynBZQGPN3siRAFmKYhU8kjZ6ef7y7Loij/Thzy5ID5IhmkCszLyIFt9WE02ImpiXPwEwpmkKazUSkrYmKE/mhp3NaFBoCEiCLvmkIxpPufgHsbQwBsOiU7ngVFRWUnZg9jmk6TqssMhYJLpeLSy65hL/85S/YzCZyXGN/FlkCZCGGWeJ2eNdvyjX+OC0NdfjcTrKzs5N3MiBNCuuLFBRFoSjNlrKahYLCqmULofE9ttWmTrNwWky0RlTU8VSQVIyKuKbTGtaS7n4B7KkLYTPD7FPzj/e/n3+cWGQs5zDxvquvvpp//OMf+P1+Cjy2MZ+HLAGyEMOsJaLjPK3m4576IDQfZfG81OXdnDYprC+6l+W0dFfIjSULF+JrP9pt22lF2k6LPgrEEgFMqhrGexpCzM12dVa3iMXjHDl8mDlzZgOg6uBzyF0w8b7s7GxWrFjBs88+S7rDjKIo6GM4zUKuwEIMI003aItoSUXR99SHsbUeZ+WS+Un7hOM6OS6ZeRHdc9nMeKxmIilmYGbPnoUt3MK7ta20d9N22mJSaA2nLgUnxEkt4Tip+jkEYhoHWyJd8o8PHTpI4ZRCnI4TM8qKgccmIYbo6vrrr+eZZ54BXSPPbSEYG7uzyPLpFWIYBeMaOkrSCvBdte04/HXMmZNc/zgx8yIBsujZlDQbwRQ5fFaLlXlls6HhMDu7SbNwWU00SD1k0Yv6QHL3T4C9DYn84wW57wfI+/fv76xeEVV1vDYzVrOEGKKrmTNnMnXqVF577TXyPDYi3RV2HwPk0yvEMGqPaJhPuxleF4jTVHWMgsJ8XE5n0j4GBh4p7yZ6kem00t3dyTNXLMHS/B7buyn3ZjObCMY0omM8B1CMnnBcJ6Ia2FIEubvrQ1hNUJZzaoBc0bngOKzqZMtdMNGN6667jieffJJ0uxmLOXGndSySAFmIYVQfiOGwpsg/bjrKkvJ5Sc+PaToumxn7OOhTL0aX02oi3WEinGIWecH8+bjaj7Otqr37ttNI22nRvUBMO723Uac99SHmZDuxn8g/bmtvp6O9nanTEg2PNEPyj0X3li9fjq7r7Nm9izyPlWA3dd1Hm1yFhRgmMU2nI6Zxehre7oYQltajrF1SnrSP5B+L/pjitRNMMQucnpZGYX4+TcePcLw9lnJfm1mhJSxpFiK1xmAMe4rqFYF4Iv944anl3fbvZ9bs2Z2pZAYGbsk/Ft1QFKVzFjnPbSOWojPoWCCfYCGGSSCmg6EkrQDfdbQJZ8zP9OnTk/aJ64bkH4s+y3BaUAwjZcH91csWQeN7bO+23JuZxqAs1BPJdMOgMaQmVd8B2NcQwgAWnrJAr6Kiokv+scdqTpmaIcRJ5513HocOHaKtvgqL2dRt86PRJJ9gIYZJazjO6etbGkNxGo4dYtqMmVjMyXnGiqJI/rHoM7vFRIbT2tmM5lRrly3C0XKYbTWpy71ZzQoR1UiZoiEmt2BMRzNI2clzd30YiwnKshPrJ3TDSDQIOVH/OKzq5LjlS77omc1m48orr+QvTz1Fgcc6JtO9JEAWYpjUB+JJtYz31Ieg6SjLF6bOP3ZYTJJ/LPql0GslmiJAnlI0Ba9NYefB4z3cwjQIjsELkxhd7VEVpZvc9T31QeZkOTtLV1ZVVeF2u8nKzAQS+cfpkn8s+uCKK65g48aNWGMBxuI6vQFfiRcsWDCU4xBiQuluBfju+iDmlmOcs2xhyn0k/1j0l89pgRPNP06loLCgvJx43SH2n2gLfDq72URjSNIsRFcNwTjuFC2iQ6pOZXOEBafkH59a3g3AMKT+seibtLQ0zj77bP75/N/HZGphjyN66qmnUj5uGAZ1dXXDMiAhJoLuVoDvOHgcj91Mfn5u0jbVMMhwjr2ThBjbbGYTaTYTEdXAae36qbtg1VL+8avH2V4XZFG+O2lfl9VEc0jFMIyU3dLE5BPXdNrCGlnO5AB5X0MIHViY3zX/+ILzzwcSd8E8Nsk/Fn137bXX8sUvfpGVl1yDPza2zkE9Xo1vvvlmPvKRj6Q8cUYikWEblBDjXaoV4M3hOPVHDrGkdDZKivDZMBTckn8sBiDbaaJZ1ZJSehbOm4Mr0syWw43cujj5S5nZpBDTdMLxRHlBIfwxHUVJ3V56d30IswLzTuQfRyIRjh87RmlpKQChuE5Rmm1ExyvGt6KiIubPn8/+LW9QvnTFaA+nix4D5IULF3LnnXdSXp5cjurll18etkEJMZ4ZhkFTWMOblH8chqYjrFx7btI+cc3AYVGSWlIL0Rdem5nmFLMvVouVaTNnsf9ABW2ROalvYyoK/pgmAbIAEouLU1R3AxIpYrOzHJ3nqYMHDzJ12lQcDgdwoguoU/KPRf9cf/31/PjHP+as1StHeyhd9Hg1/slPfkJaWlrKbX/5y1+GZUBCjHfBuI6mG0krwHfXdqC0VnPu8uT8/bCqkeOWC4sYGJdVwayk7ki1ZtkiaDzEzrrUechOs0KTlHsTJyTaSyd/WYqqBpXNERaekqqzv2J/Z/UKAAXJPxb9N3/+fKZPn05TU9NoD6WLHj/JZ511FlOnTk25bfny5cMyICHGu/aImjL/ePv+g2RkZePzepO2xXSDTMk/FgNkUhSyXRbCKZqGXLRqCabmY2ytak+5r8Niojmsoo/BOqRiZIXiGlHNwGpOPoMdbougGbAg79T20vspO9Fe+mQXUMk/Fv2lKArf/OY3ycnJGe2hdNHjFfmzn/1sjws37rvvviEfkBDjXWNQxXHaPcq2iErdkUpWzpmTch9F8o/FIOW4bdQHQnhOSwH1paeTlZ3Dlr0VGGcWJeW/m00Kqp7IH5Ua3JNbIKZ325i8simCCZifkwiQm5qbCYXCFBUVAYnPzxTJPxYTSI8B8qmzxN/61rf4zne+M+wDEmI8U3WD1ohKpqNroLG3IQxNx1h9+Y1J+8Q1A7vkH4tB8tjM3QY3Cxcs4JV3KznefjZT0+1J2xUFOqKqBMiTXGMghjPF7DFAZWuE2dnOzu56Bw4coKysrLO9tKZDhuQfiwmkxwB53bp1nf//Jz/5SZefhRDJEt2AkktmbTvWBMFmzl5SlrRPWNXIPX3aT4h+clpNOCyJqhSn3+a+aPUSXnnj52yrCaQMkJ0WhcagSqE3eZuYHHTDoCmskpbiS1JE1TnWFuHssuzOx/bv20d5l34IBu7TW4cKMY71+dMsNTKF6F1bRMWUIgN5x979ZBdOw2VPDoTjukGW5B+LIZDnsRJK0Tp60ewS7CadjfuPptzPYTHRGlFTLvITk0NP7aX3N4XRdFiYm0iv0HSdysrKzgV6MU3HaTNLF1AxocinWYgh1BCI4zptFiUY06k7fLDLau8uDCXlqnEh+ivTaSXFOj0UFKbPnse+d/ambDttUhR03SAYl7bTk1VicXHqL0i760IoCsw7ESAfO3aUdF86vvR0QLqAiompxwDZ6/WSlpZGWloau3fv7vz/Jx8XQrwvquoEY1rSLMqhlgg0HeXMpcnl3U7mH5/e4EGIgThZYuv0ttMAa5YvQq17j33dtJ1WFOiISIA8WdV3014aYE9DkOJ0e2cKxentpeO6MSZbBQsxGD1elf1+Px0dHXR0dKCqauf/P/m4EOJ9gZiWcv5l77F6MDRWzS1J2hZRdbJcsrBFDA2r2YTPaSaaYpb4kpULIdDApsONKfd1W800hKQe8mQU13Taoyr2FAv0IqpORVOYWVmOzsf2769gbtncLs+TBZ5iopFpKyGGSHNYxZbiAvPuocPkTp2ZskpFTNcl/1gMqVy3NWWqRLrTTlbRdDbt2J1yP7tZoT2ioUoe8qTjj+lgKN20lw6i6jA3O5FeEQqHqa2pYcbMmcCJ/GOr5B+LiUc+0UIMAcMwaAwmd6AKxDWaa48zb27q/GND6h+LIZZut0DKVjWwYMEC6g4doC2iJm1TFAXDME5UYhGTSUsoTndZXttrg9jMMCMjMYP87oEDlEyfjs2auPMVjuvkuuVLvph4JEAWYgiE4zoxTcdy2grwvbUBaKvnrGXlSfuouoFD8o/FEHPbTJi6aTt90col0HSUrdWpU+TMSmIWWUwuDSm+3J+0rSbIglw3JzdXVFQwb9776RWqIfnHYmKSK7MQQ8Af0zBSzNq9ubcSHG6WleQlbQvHdTKlsL4YYiZFIaebttOLS3KxeDJ4fcf+lPu6rCYagrHhHqIYQ0JxjYimp2wv3RCMc7wjxrJCNwAGRpf20glyF0xMTBIgCzEEmoLxlB2odu7eS3ZBcer8Y80gW0ojiWGQ47YRVZNnkM2KQsmcuezdsxcjxZJSu8WEP6YR01LUihMTkj+q0V1KzvbaAABLCzwANDQ0omkaBQX5QCL/2G6WLqBiYpJPtRCDdLID1empEm0RlabDFcwtm516RwVcNvkTFEOvp7bTq5cuJlRdydG2aOonGAqBmATIk0VjN1/uAbbVBslyWpjmSzQ4qqiooKysDOVEQB1RdXLdchdMTExydRZikE52oDKdtgL8tf3HIBZmRVlJ0j6qbmA1KThl5kUMg1PbTp/ugsWloGu8uvdIyn0tJmgNS7m3yaC7L/cAmmGwszbIkgJ3Z0BcUVFB2Wn1jzOkCo+YoOTqLMQgtUdTd6B64+3tWAtLmeZzJG0Lx3WyXBZp4S6GTZ7HSjhF2+l8j4204lm8vX1nyv0SecjJVS7ExBOIaei6kfTlHuBgcwR/TO/MP9ZUjcrKSubMef+OmFThEROZBMhCDFJDig5UBgaV+/dQNm8BphQxcCL/WG5NiuGT4bSSIg0ZgPkLF3HswD7UFB33bGYTEVUnkqpntZhQ2iMaplQnKGB7XSL/eEl+IkCurqkhNzcHr8cLJLqAOiySfywmLvlkCzEIcU2nLawldaDaX9VEtK2Zs5fOT7mfoRi4Jf9YDCOvzYRhpG47ffaSeWiBFnYcrku5r4FBUOohT3j1gRiubgLcbTVBSjMdnSXcjhw5QlnZ+/Xcw6pGjuQfiwlMrtBCDMLJxUynp0o8v2E7ZE9jRbEvaR9NN7CZTJJ/LIZVT22nlxR6IWc6r2zakXJfm0mhOSxpFhNZTNPpiGkpZ4CDcZ39jeHO9ApIBMhzT8k/jukGmZJ/LCYwuUILMQit4Tip4txdu3aRWVJGvid5hiUU18mU/GMxArprO+1zWMifOZe9u1O3nXZZzTQFZaHeROaPaihG6nPQ7vogmgFLT6RXBAIBWpqbmV4yvfM5iuQfiwlOAmQhBqE+qOI6bQW4PxSmoeoIZyxZkHKfqGaQI/nHYgSk2y0pG9gArFhcTlPtMdoCwaRtFpNCVDMIpQiuxcTQElbppnke22oCOCwKc3NcAFQcOEDx1KlYLIkZ47hmYJf8YzHByadbiAGKqDrhuI7N3PXP6IUNOzB8U1gxLSvlfopi4Jb20mIEuG0mzN20nV5WnAEZRby4MXWaBUaihKGYeAzDoLGH9tI7aoMszHNjO7G2omL/fkpKSjq3R1SdbMk/FhOcXKUHINWiFzH5BKIaipL8WdiwdQdK/kwW5bmStmm6gcVkSll3VIih1lPb6QV5bpS8UjZs7SYP2azQGJI0i4koHNeJajqWFBUs6gJxqv1xlhYk0iviapw9e/Ywa9aszufEdJ1Mh+Qfi4lNrtID8E5jKOWMjJhcGkNxbKauf0JxNc7hygOUzp2PJ0V+XljVyXKaJf9YjJhst41IinpvbquJmXPnc/Tgu8TV5EDYZTXRHFJlQmAC6uihvfS2zvbSiQB5//79FBQWkpbm7XyO1D8Wk4EEyP20b98+du0/iF9KIE1qhmHQFErOP971TgVhh48V0/NT7hfRDLLdtpEYohAAeHooJ7isJIewM4td7+xP2mY2Kai6TihFsxExvjWG4ji6aS+9vSZAjstCcXriPLVly1aWL1/euV3VE/WP5S6YmOjkE95PtbW1vPzP12iRW4+TWiiuE9d1zKfdonx543bIK2XpKeWRujAMPHJhESPIZTXjsCjEU5R7W5TvhrxS/rFxW8p9DUPBH5XJgIlE1Q2aQ6nbS6uGwc66EMsKPSgoRCIR9u/fz+LFizufk+gCKvnHYuKTK3U/rVmzhqPHjnKwtmW0hyJGUUdURTntFqVuGOzdsxtHYSllWc6kfTTdwGqW/GMx8vI81pQVKeZlO7EUlLJnz170FKkUTotCk0wGTCjtERXdSN1e+t3mCMG43plesXPXLmbNmoXH/f4X/piukyX1j8UkIFfqfrLaHcyYVcbb23dKCaRJrCGoJt2iPHz4MH7DxuLS4pSLXyT/WIyWDKeVVJkSDouJedMKCFucvHfoUNJ2p9VEc1hNGTyL8anGH+u2SdH2mkT+8eIT9Y+3bt3KsmXLTnuW5B+LyUEC5H669pEK9luL2Llrl9x6nKQ03aAlnHyL8s0tO4hmz2BpgSflfhHNkFuTYlR4bSbASLngblG+i4BvOpt37EraZlIUNCn3NmFEVZ3GUDxp7cRJ22oCzMlykGY30+H3c/ToURYseL+eu6ob2M2SfywmB/mU99PSAjfHySbc0c6Od4+O9nDEKAjENDjtFqWBwaZtOyC3tPP25OkUjJSVLYQYblaziXSHJWXb6ZN5yJu27sAgebuCQUdU2k5PBG0RFcVQUt7FCsQ0DjRFOr/gb9+2jfLycuy29xcVh+M6mU75ki8mBwmQ++nMqWmoKBTNnss/394m5d4mofaIlpS/V1dXR2swSk5hEVPSki8gmm5gVpRuZ26EGG553bSdnpPlxJ6RS3tEpbq6Jmm702KiMSgB8kRQ7Y91ew7aWRdCB5adWGC8ddu2LtUrAGKaQbZL8o/F5CBX635aXexFAYyCuezctYv2iCxgmWzqg7GkW4w7d+4mmDG9c/X36SKqTrbLIvnHYtSkO1K3nbaaFBbkuYlmz2DPnt1J250WE60RVSYDxrlwXKctrHWbHrG9NoDTolCW7aShsZHm5mbKysq6PkkBVw9lA4WYSOST3k8em5nSLAeVMTc+r5e3tiVfUMTEFdN0/DENx2mLXN7asp149sxu0yvCqiG3JsWo6qnt9OJ8N21pJWzeltxVT1EUMIxEapEYt1rCcbr7fm5gsK0mwKJ8NxaTwrZtW1myZDHmUxohqbqB1aR0u8BPiIlGPukDMD/HSWVzhEWLFvBSN/VDxcTkj2ooRterTGtbK8frGiFzSiKfMxXFwGuX/GMxekyKQrbTQiRF2+lF+S7IKKSmoYWm5uak7YqinOi+Jsarqo5YtzXYa/xx6oMqywo8GBhs3bKV5ctXdHlORDXIdlnlLpiYNIY1QG5ra+OGG26grKyMuXPnsnHjRlpaWrjooouYNWsWF110Ea2trUCiM9nnPvc5SktLWbhwIdu3bx/OoQ3Kgjw3BuAomsveikpaOgKjPSQxQlrCKtbT4tzdu/egZU9nVrab9BRBsG4YWCT/WIwB2W4r4RQL9WZkOPA4rCdqIiffFXNZTTQEJZ1svArGNAIxDXu35d2CACwtdHP8eBWqqjJ9ekmX58Q1yJL8YzGJDOsV+/Of/zyXXnopFRUV7Nq1i7lz53L33XdzwQUXUFlZyQUXXMDdd98NwHPPPUdlZSWVlZXcf//93HHHHcM5tEFZmOfEZoKKDpg+vYSX39g02kMSI0DTDWoDcdynRcjbd+6k2Tutc3HL6SKqQZZT8o/F6PPazSmykMGsKCzKc9HsncauXckBst2s0B7RiGtS7m08agqppCjN3mlbbYB8j4VCr5Vt27ayfMXypLUUBgZuyT8Wk8iwfdrb29t5/fXX+eQnPwmAzWbD5/Oxfv161q1bB8C6det4+umnAVi/fj233HILiqKwatUq2traqK2tHa7hDUq+x8bcHBc760KcsXQxL721ZbSHJEZAR1RD1bq2lw6GQuyvPIyRXcKSbtIrIipS/1iMCS6rGbu5u7bTHtrcUzh89BiBQNe7Yie/3AWkHvK4YxgG1R1RvN2UmFR1g511QZYWeDAM2LZ1GytOS6/QJP9YTELDdr/k8OHD5OTkcOutt7Jr1y6WLVvGT3/6U+rr6ykoKAAgPz+f+vp6AKqrqykuLu7cv6ioiOrq6s7nnnT//fdz//33A9DY2EhjY+OgxnkyxaM/tIjG/DSNFxvaycwtoM4fZv+BA2RnZg5qLOPFQN6zieBQW4xQWKM19v5F4p139pExtRTVoVFojdHaFkvaLxjwE/HbaYzIxaWvJutnbKD6835ZY3FqQippp80GznbHybaq5JTMYdv27SxcuKDL9nBMZ/cRP/Oy7UMy5tE0mT5f/phOfXOETKf5/7d33/FxlVfi/z/33umj3mXJtmzLVZYtF2xMd8X0hJie4AQICSSEELJhk2x2k93kF8LmS8IGsrukEEMSIDgsECAUN0qwca/Yxk22ZUtWL6Ppc5/fH2MJC6taM6rn/XrxSpDu3HnmQXPnzLnPcw7N7fz+UK2fhIiXacmJ7Nq1i/T0dOwOO3X1n8yRJ2hiDTVTXV3ddwMf5IbT31isDLQ5i1uAHA6H2bp1K7/61a+YO3cu999/f+tyihaa1n7B8s7cfffd3H333QBMnz6dzMzMXo+1p+dICpsUjdL484EAx4JOxo0r5MOtO/niLTf0eiyDRSzmfTCJmIqPmhvJcxttMsh7P/qI4/YRjMvKIDM9td3HJQVMRuVmnVU7WXRuuP2N9VZ358tICNNU3kzqp9aTpqQoTHsD9Ykj2bdvH5deckmb36cClc0hbIkJJDsG/1rU4fL31VjrIz3VQUoH/832lFZRq5zMHpvDyyvfoXjaNFJT2l7LIr4QeRbbsJmzWJH56rmBNGdxS2nl5+eTn5/P3LlzAVi2bBlbt24lOzu7delEeXk5WVlZAOTl5XH8+PHWx5eVlZGXlxev4fWK3aIzMd1Jkk1nW0UzM2fM4O33NrTbxlUMDQ2BCBGTNsFxIBhk10d7qU0YzcwO1h97QybpTl2CYzFgJNp00M5uO62hMT3HxXF7Hgc+/hh/IHDWY11WnSN1/r4aquglUylONoU67eC5tdzDxAwHdt1k544dzJ416+yDFB1WwBBiqIrbX3xOTg4jR45k//79AKxevZopU6Zw7bXXsmLFCgBWrFjBddddB8C1117L008/jVKKDRs2kJycfNbyioEky22lKMvJ9nIvk8aNxqPZOHDgQH8PS8RJRVMQu9E2yP344/3Y0nLA5mRmB+uPAxGT9CGQbRNDh9XQSbIZHbadbjCtpOeOZN/evWf9PsFmUO0LU++XznqDQcu+CUsHO/QaAxH21/iZNSKBPXs+YkReHikpKW2OCYRNEmwGNkO+5IvhJa6f3L/61a+47bbbCAaDjB07lqeeegrTNLnxxhv53e9+x+jRo/nLX/4CwJVXXsnrr79OYWEhLpeLp556Kp5D67VUp4UpmS7WlzVzyhOmeMYc3nh7NRMmTOjvoYkYC5uKyuYQKY62WZgd23cQzigk3WlhVIqtg0drJMjObzHAZCfYOFjrO6vhTcnpL3rWvPHs2LmTkpKSsx7rtkSzyCU5bqnMMsCd8oSwdhLYbitvKe+WwAcvbz6rtTRE74IVpDogJHcOxPAS1wC5pKSEzZs3n/Xz1atXn/UzTdN44okn4jmcmHLbDKZmuYEqtlU0M2NGCX989N+59ytfxmKRjOFQ0uAPYyraLJOImCa7d++mbNL1nJ/rbre9dDBi4rTqZ2WehehvyQ4D2vmbzUmwkpNgod5WQNU77xCORLAYbb8Yum0Glc0h6v0RUp1yrRuowqaiwhMkqbPlFRXNuK06o9zw+337uOWWW846xlSKFIdBUMpgi2FGUlvnyGHRGZlsI9ttYXtFM7mZ6aTmj2n3C4EY3CqagjgsbYOJw4cPYXEn4bEkdrr+ONstAYQYeNxWA02LBj+fVpKTwL4mg/SMdA4ePNjB43UO1/lk38UA1uAPn7Vv4kwt7aVn5LjZvWMHEyaMx+1ytTnGVAo0rdM1zEIMVRIg90Km28rULBc7K7xYDY3Jsy7g7VWr+ntYIoZCEZNKb/isLng7duzEljseoMP1x2FTkeqU+sdi4DF0jTSnBV+o/bbTnpBJztjJ7Ny5o93Hu20G9f4IdX5pPz1QtffF/kzHG4JUe8PMHOFm8+bNzJp19vIKX8gk3WXpMMgWYiiTALkX0pwWJmc68YZNDtcFmDJ1Kpt27KGpqam/hyZipMEfQSnVZnmFQrFz5w5qkgoYm2pvt3ySUgpN00hsp/W0EANBpsuCv4ONegChrEJ27tyFov0ssduqc6hWssgDUTBicqr57C/2Z9p6ev3xhASTY8ePUVxcfNYxvrAiS5ociWFKAuRecFkNirKit6S2lXtIcDqZVHIe7733Xj+PTMRKuSd4VveosrIyQOdgMIGZuR23l051WDrcPS5Ef0u0W2gvtk1zWBiVbONQwIXNZuXYseNnH0T0+tfoj1Drk4oWA029L4ymqU7LS2452UxeopXj+3cydepUbNb2AmFFknzJF8OUBMi94LTqZLqsjEmxsb3Ci8uqM/m8C3n77bf7e2giBoIRk+p2llfs3LmLtDGTiBDd/d0eX9gkU9YfiwHMZdUx9Ggzm08ryXGzp8pHUXFxh8ssIFr27VCtX7LIA8zJplCnbaGDEcXOU83MyHVHW0ufd95Zx4RNhc3QcUr9YzFMyV9+L6W7ouuQP6ryooD8MYWcqKjk5MmT/T000UsN/gigzipltWPHdgIZY7EZUJTpavexCoZEtzExdOmaRobTgj/c3jpkN4GIImnkJHbu3NXhOZxWHU9QssgDiT9sUuvrPED+qMpLIKIotPupqa1lwoSJZx3jDUXISrBKKT8xbEmA3EvpTguTM12ETdhd5cUwDOZePJ81a9b099BEL51sCuIw2r5FqqqraGrycDCSytQsd7sl3CKmwqJruCXzIga4dJe13XXI07KjX/xOGek0N3s4VVnZ4TkSbAYHa/3tVsQQfa/WFwJN6zSw3VrejKGB//heZs2ciaGffa0KRhTpUsZPDGPyCd5LbpvBxHQHFg22lzfjsOgUzYkus5DbjoNXIGxS4wudtbxi165djJs4heNN4Q7XH3tDJhkui2RexIAX3UR69nUq0WZQmOZge6WXacXT2LVzZ4fncFh0moMRarxSKHcgONEY7LIt9NbyZiZnONi5rf3mIC1kk7EYziRA7iWnVSfZYWFShoNt5V6cFh1H+gjsDgd79uzp7+GJc1TvD6Ops7MwO7bvwBgRLe82o4MAOWiaZMrObzEIuKw6Fl3vYB2yi31VPiZNLWZHJwEytGSRA5JF7mfeYITGQOSsDolnqveHOVjrZ5ylEdNUjC4YfdYx/rBJot3AZkiIIIYv+euPgTSnlSlZLg7V+WkKRjCBC+cvardjoBgcyptCOK1tg+P6hgZOlpdTYcshxWEwJtXe7mOV0kiQzIsYBDRNI91lwdtOPeSSHDdhBcGUPE6dqqC+oaHD8zgsOt6gSXWzZJH7U40vjNHFnautFdHybtrJfcyePbvdLqDesElOgi0uYxRisJAAOQbSXdF1yAA7T3nRNSiZexHvv/8+wWCwn0cneqqjTS7r1q7lvDnnsaMqwIwcN3oH7aVdNr3TDI4QA0mmy0qwnXXIU7NcGBrsrgoyZfIUdu/e3el5kuy6rEXuR0opyhqDJNg6v/ZsOdFMghWO7dvZ4fIK01Sn25ELMXzJp3gMuG06Y1MdOC0a2yuacVt0grYECgsL2bBhQ38PT/RQvS981iaXZq+X9evXM27mRdT7I8yS9tJiiHDbdJR2dlDrsOhMynCyvaKZ4mnTOi33BmC36PjCJlWSRe4XzSETXzjS6bIIf1jxwfFGphi1JCcnk5OdfdYxplLourSXFkIC5BhwWnScFp3ibBfbypuxW3SaAhEumb9QllkMQieaArg/lQF+7733KJpaxGFf9LZjSQfrj6W9tBhsnBYdu6ETbmcd8vQcNwdq/IweP4mjpUeprqnp9FzJ9mhd5PbWNIv4qmoOdrm8YvNJD76wIrF6P+ed13722BcyyXBaOm0yIsRwIAFyDLSs4yvKdFHuCVHhCYEGU2fOYffu3dTV1fX3EEU3+UImDX6zTXH8QDDIO++8w+LFi9lS7mF0so2MdoLglvbSknkRg4mmaWS4rPjaWYc8I8eNAg40RLjwogtZ1UUTJJuh45cscp8zleJEY4gEa+fXnrVH6kmxKqqO7GPmjJntHuOLKDLd8iVfCAmQYyTDZWVShhOA7RUe7LpOk7Iwd+5c1q1b17+DE91W5w/x6cTJ+vXrGTOmgNSMHPZUepmZ23H3PGkvLQajdKeFoHl2gDwxw4nN0NhR0cyC+QvYtm0btV184U+SLHKfawpECEZMrO3UZW/hCUbYeMJDERXk5+eTkpLSwZFKyrsJgQTIMeO26uQl2Uh1WNh2uu10tTfMwkWLpGnIIHKiMdimwUc4EmHNmtUsWbyE3VXNhEw6rH/sCyuyE2T9sRh8EuwGSp0dXNkMjaJMF9srvCQkJDDvggt46623Oj2XzdAJmCanmmWDcl+pbA5h7eKL+fvHmgiZoJ/Yzdy557d7TCiicBg6ri4y0UIMBxIgx4jTqmM1dKZlO9le3oymR9ejFk4upq6ujkOHDvX3EEUXvKEITQGzTQWKLVu2kJGRQUFBAf842oTDolGc3VF7aUWSXQJkMfg4LDoOi0Yw0l7baRel9QHq/WEWLlzAtq1bqa+v7/R8STaDw7WBdtc1i9iKmIrypmCXS7vWlTaQpXlpqjzJjBkz2j3GG4qQJcsrhAAkQI4ZTdNIdxoUZbloCEQorQ+goWgMmtxwww08+eST0llvgKvzhdssrzCVYtWqVSxevJiQqXj/WCPn5ye2W8ItYiqsun5W5z0hBossd8frkAG2V3hJTEhk3rx5vPV211nkYMTklEeyyPHWEIgQNsHoJINc4wuxvcLLqMb9nDdnDjZr+0FwyIQ0aXIkBCABckylu6xMTD+9Drm8GbfVoMIT5Oqrr6apqYl33nmnn0coOlP2qRatu3fvwmKxMGnSJLae9NAUNLmsIKndx3pD0e550l5aDFapTguhdjK+49IduK06O043mFiwcCFbNm/ptHEIQLLdwuG6AKF2stIidk42BrB3svYY4N2jTWBG8Jbu4sILL2z3GKUUaIrELuooCzFcyDshhhJtBhluC3mJVraVe3FYdDyBCGGl8bWvfY3f/va3eL3e/h6maIc3GKE5FMF+OjusULz11tssXrwIDY13jjaSYNOZNaL9DXoB0yRT6h+LQayjW/QWLbqsqCVATkpMZM7cuaxatarT81kNjbCpON4QiPlYRZQ3GOFUc7jL5iBrjzQwwneMUSNy2q19DBCIKJLtFqzSXloIQALkmHJadQxdpyTbxa7KZsKmQmnR3cNFRUWUlJTw5z//ub+HKdpR4wu36Yt38OBBvN5mSkpm4A+bfHC8iYtGJXW8EUZ1HGAIMRjYLTpum9HuOuSSHDcnPSFOnl4ysXDhQjZt3NhlFjnVYXCkPognGInLmIe7E01BrDqd3rkqawrycY2f5MqPuOiiizo8rjkUIVvWHwvRSgLkGNI0jQynweQsN/6wYl+1D7uuU3m6Juidd97JW2+9xdGjR/t5pOLTyhqDJJ4R4L755lssWrQIXdPYeMKDP6w6XF4RCJu4bUZr9lmIwSrTZcHbzjrkC0YmYtHgr3uijUJSkpM577zzWLOm80ZIuqbhtGh8XO2TPRgxFgiblDUGSeqiJNu6Iw3QXIfuqWb69GkdHqfQSHbIXTAhWsgneoylOa2MT7cDsK28GZdVp8obxlSK1NRUbrvtNn7961/Lh8UA0hyM4At90qL12PHjVFRUcN555wHwTmkDqQ5Lh9UrvCGT7ATJvIjBL9VpJdLOpSnLbeXywhTePFjPKU/0C/+ixYv5cMOHNDY1dXrOBJtBnS8SbaAkYqZlPjvreKdQ0eUV9fu4eN75WC3tX6cipsLQom3HhRBR8m6IsUS7gdtmMD7NwfaKZgxdIxxRNAejWZmrr76axsZG2bA3gFR7w+hnLJ14++23WbBgPlaLFU8oWlz/koLEDtu4mkqRIpkXMQQk2HQU7X95v2lqBmjw3O5qIJpFnjV7FmtWd55FBkhxGByo8REIy4a9WAibiqP1fpK7yB4frPFzot6LcXIvF3SwOQ+iTY4yXdJeWogzSYAcYy6rjkXTmJ7jYl+1D1/YRNMUDf4wAIZhyIa9AUQpxYnGQGv1ilOVlRz4+GMumHcBAOtPF9e/rCC53cebSoGmtVmeIcRgZTV0Em0G/nYC2Sy3laWFKbx1qL41e7lk8RLWr19Pk6fzLLLV0FDA4Tp/PIY97FQ1hwibqsuunWuPNKJXHmLy2JFkZWZ2eFwgrMh022I9TCEGNQmQY0zTNNKcFqZkuYgo2HWqmQSrwbGGT4rmT506lenTp8uGvQHAEzTxh1Xr8orVq1dx8SWX4HA4AHintJFst4VJGY52H+8Pm6Q5LZ3WIBViMMly29qthwzRLLKmwbMtWeSUFGbMnMnq1V13C02xG5xoDFJ/Olkgzo2pFEfq/F2uPY4oxTtHG8mo3sv8Sy/u9FiFbDIW4tMkQI6DDJeVMcl2rDpsK/dit+j4I4pj9Z9kT+666y7ZsDcAVHuDtFQ1qq+vZ8f2HVx6ySXRf/eH2VrezKUFyWi0HwD7woosKe8mhpBkh0Gkgz0SmS4rV4xP5e2D9ZS3ZJGXLGH9Bx/g8Xg6Pa92+k7L3iofEemwd87qfOE2X+o7suuUl5rKCtyheoqLO96cF4yYOCwaTmlyJEQb8o6IgwS7gdXQKcpysf107dC0T5U7Sk1N5dZbb+W///u/ZcNeP1FKUd4UIsEazZysXbeW8+bMISEhWuv4/WNNmMBlY9qvXnH6LNJeWgwpCTYDTdM6vC7dNDUdQ4c/76oCIC01lRkzZrB23douz+206vhCEcoapcPeuVBKcbjOj7sbweza0kYsJ3az+NKLsBgdZ4dlk7EQ7ZMAOQ5cVh1Dh+nZLo7UB6jzh9E1DZdFY1+1N7puFbjmmmtoaGiQDXv9pCkYwR9RWA2NZq+XDes3sHDhgtbfryttYGSSjTEp9nYfH5b20mIIsugayQ6DQHvlLIAMp5UrJ6Sy+lADJ5qige6ixYt5/733ae7GvopUh4XDtT68Uhu5xxoCERoDZpfZ3mBE8d7hWhKqP+aSizrenAcQNqPVl4QQbcknexzoLeuQM6NlwVqyyG6bQYM/QvnpDxXDMLj33ntlw14/KWsI0lLV6N1336F42jRSU1IBqPKG2F3p47IxSR0vrwiZZLqlvbQYejJdVrydVJy4sSgdiwHP7oquRc5IT2fa9OmsXdt1FtnQNayGxoFav9w966Fj9QGclq6vN5tOevAe28ukcWPISE/v8LiW+e+qE58Qw5G8K+Ikw2VhRJKNBKvO9opPgt9Uh4WPa/ytm2CKi4uZNm2abNjrY/6wySlPtDmIPxDg3XffY9GiRa2/f/doIwCXdlC9AiBommS4ZHmFGHqSHQaqk3XC6U4rV41PY/XhBspOf+G/fMkS3n/vvW5lkZPsFqqbQ1R7pTZydzUHI1R5Q91aXrHuSAPWE7v4zJLLOj3OH1akOA1pLy1EO+RdEScJtmhNyWk5LraVe1pri1p0DYsGB2s+6SzVsmHv2LFj/TnkYeWUJ4SmaWiaxvr1HzBu7FhysrNbf7/uSCOFaQ7yEzsufaSkvbQYotxWAzpZhwxw49R0rIbGsztPZ5EzMpg6dSrvvLOuW8+R7DDYV+0n1E5ra3G2440BrLrW5R2r5pDJ+r2HScFHcfHUTo/1hqW9tBAdkQA5Ttw2HU2D6dluKpvDrTu+AZIdFk6dkT1JS0vjlltukQ57faSlyH6S3SAcDrNmzVoWL1nS+vuypiAHav0dtpaGaJvXBGkvLYYoQ9dIdVjwdbLMItVh4eoJqaw50sDx05vullx+Oe++8y4+v6/L57AZOhFTcbQ+ELNxD1X+sMnJbrSVBlh/vJFw6U4uvegCDL3z65NS0l5aiI7Ip3uc6Fr0A2ZKVnQd8uYTzW1+n3I6exI8nT259tprqa+v59133+3zsQ43Nd4QYRUtsr9582ays7IYPWpU6+/fLW0A4NJOAuTozm8prC+Grkx35wEyRNci2wyNP++MVrTIysxk8pQprFu3rlvPkeIwOFofoDEgtZE7U94UQNe0bnW6W/VxNdZTH3P9kks7PS5iKiw6sslYiA7IOyOOMlwW0hwGY1PtvLi3mtAZa/pasidHTneWaumw95vf/EY27MWRUorS+gAJVgNTKVatWsWixYs/+T2KdaWNTM1ykunq+NZjtL20LK8QQ1eS3YLqYINqixSHhWsmprKutJFjDdFM8NKlS3ln3Tv4/V13zdM1DZdVZ3+1r7W6j2grFDE51hDssq00QJ0/zPZtWxkzdgxpqamdHusNRfdQSHtpIdonAXIcJdktoGl8qSSLCk+Y1z+ub/P7VIfB8TM6S8mGvfir90doDkZwWHR27tyJ3W5n4sQJrb8/Uh/gWEOww9bS8El7aVl/LIYyt03H0OgycF02JR27RePPpytaZGdlRbPI3Sxf6bYZNAY+qe4j2qpsDmGadKtb5zuljXBsJ9cuuqzLYwMRkyxpLy1EhyRAjiOXNboOeeYIF8VZTp7dXdXmlqWmaSRa23aWkg178XW8IYDDomMqxVtvvcnixYvblHF7p7QRHbhoVGKH5/CFTNJd0l5aDG0t5So7ajvdIppFTmNdaWPreuLLlyxh3bp13coiQ3Q984Faf5fPNdxETEVpXYBEe/c+qt/auh+n6WfB3JJuHK2R0I2stBDDlQTIcWToGil2C4Gw4o6Z2dT7I7y4t6bNMU6rjj9ktnaWSktL4+abb5YOe3HgPaNM0ocfbkDXDaZNn976e4XindIGZuS6Selk44ovrMjqZPmFEENFpsuKv4OGIWf63OQ0HBaNP53urpeTk8PECRN4973u7amw6Bq6BofruhdQDxe1vhD+iNllW2mAck+Iwzs2MWvu+V0umwhGos1GHLLJWIgOybsjzjJOb3SZnOFkXn4CK/fUtC6paJHiMDhU66P5dGep6667jpqaGjZt2tQfQx6yTjYFseoaXp+PV175GzfeeGObD5J91X4qPOFOq1dEqW7tJhdisEu0G60lKjuT4rBw3aQ03j3aROnpLPLSK5ayZs3abmeRk20GJ5uC1Ppkwx60tJUOkNjNpVyrPq6Eio+5+YrON+fB6U3GbqleIURnJECOsyS7QUsi+IslWfjDiud3t80iG7qG3YhuVFFKYRgGy5cv56mnnsI05ZZjLAQjJmVN0cYgr776KtOnT2fUyJFtjnmntAGrDvM6WV4RNhU2Q++y1asQQ4HLqmPRtNYlYJ25fnI6TovGn05XtMjNyWXSxIndXousaRrJdoO9lV78XVTPGA7O3C/RFYXijXfWkzFyLIW5mV0eHzYVaXIXTIhOyad8nLmtBtrpgvujU+wsGpfM3z6u5VRz2w5SiXaDOl+EitP1ki+44ALsdnu3WreKrlU1hzAVnDhRxvbt27nmmmva/D6iFO+UNnFeXgIJ1o4zNt5QhKwEaS8thgdN08hwdV3uDSDZbnDdpHTeO9bEkfpo1njpFVewbu3abtVFBnBYdJSm2FXRPOwbiBxtCODs5hKIQ7V+avZt5bJLLuryWFMpdNlkLESXJECOM0PXSHEY+MPRDMznp2WiAc/sqDrr2BSHwcc1PvxhE03TuOOOO3j66acJBmV3d2+Y6vRGF6vGX/7yF66++mrcLlebY3ad8lLnD3davQIgGIF0p9yaFMNHejfXIQNcPzkNl0XnTzuiFS1ysrN7VBcZIMlmwRtW7Kn0ditzPRQ1BSLUeEO4uxnEvvzhXogEWXbJjC6PbQ6aZLmtWGSTsRCdkgC5D2S6rPhOZ0Oy3VaumZDG6sMNrWv1WliNaD2FQ7XR7Mu0adMYOXIkr7/+el8PeUip84XxhxVbN28EpZg3b95Zx6wrbcRh0ZiTl9DheaKZF6TzlBhWEm0GWjfWIUN0Sdl1k9J4/3gTh05vuGupi+z1dS+LDNESmLX+CPtrfMNys/LxhgB2o3sBrIni/fffZ8y080hxdL1sIhAxyUmU8m5CdEUC5D6QaDfaZEJuKs7AadFZsb3yrGOT7QblTUFqTreh/tKXvsRzzz0nzUN6obTejxb288orf+OGT23MAwiZivePNTIvP7HT9X7NQZPsBMm8iOHFadWx6nq3s7nXT0nDbdVb1yJnZ2Uxtbi4x8vF0h3Ra2FLwmC48IYiVHiC3d6ct7W0Bl/Zfq5Z0PXyimj3PK1bTUeEGO4kQO4DSXYDl9Vo3XiSbDdYVpTG+jIPH1W3zaq0bFTZV+UjFDEZN24cM2fO5MUXX+yPoQ96jYEw9f4Ia956nenTp7dpKd1iy0kPnqDZZfWKQMQkR9pLi2FG0zTSXRa83axRnGgz+MykND447uFgSxb58st57913ae7BF31N00h3WjhaH+B4Q6DrBwwR5U1BdF3r9j6Hlavex8gqYMGk3C6PbQpGyE20SQ13IbpBAuQ+oGka41LteE6XcQP47OR0UhwGv9966qwySnaLTtBUrTVBb7/9dl5++WXq6ur6dNxDQVlDkOpT5WzbdvbGvBbvlDaSYNOZOaLj5RURU2ExdJKlvbQYhjJdVoLdXIcM8NnJ0SzyH0/vtcjIyGDa9GmsWbO6R8/b0qxkf7WPCs/Q34sRCJscbwyS3M3scTBisnvzBmbMuaBb1S5CpiLLLdUrhOgOCZD7SLrLis3QCZ3+kHFadG4tzmR3pY/NJ5rPOj7NYXC8IUiFJ0hOTg7z58/nueee6+thD2q+kEl5U4DX/m9luxvzAPxhk/VlTVw0KglrJ1mVpmCEvERrlwX4hRiK3Dajm6uQoxJs0btkG8o8bCn3ALBkyeW8/977eDyeHj23oUeD5D2V3iFZI9lUinp/mP3VPjaUNaGjdTvD+/zb/yCsFNfMm9blsaGIwmnRpYa7EN0kAXIfMXSNMal2Gs7IIl8xPoWcBCu/31aJ+amPH03TSHVY2FvpxROMcMstt7BmzRoqKir6euiD1qnmINu3bQPTbHdjHsCHZR78YdXl8oqwgkzJvIhhKtp1TWv9gt8d10/OYESClV9vrCAYUWSkpzNj5kxWrVrV4+dvWTe7o8JDY2DwB8lKKTzBCIdrffzjWBPbTnqoag6SZDNI6eZdqurqal7+vxdJmXMVszq5+9WiKRRhRJJNSlQK0U0SIPehLLcVHVo3u1h1jdtLMjlSH2BdaeNZx1sNDYdFZ9cpLwlJyVx33XU8/fTTfTzqwSlsKvadrGf166+0uzGvxTtHG0hzWCjOPju73CIUUTgMrdubZoQYijLdVryhSNcHnmY3NO6dm8OJphArP4qWfbv88iWsX7+exqamHj+/zdBxWw22lzfjDXZ/HAOJNxShrCHAhjIPG080cbwxiNuqk+6ykmS3dDtzHI5EeOx/f4cnfxbL5hV1a+NwxFRkSHMQIbpNAuQ+ZDV0RqfYqD8jA3JZQRJjUuw8vb2KUDu7xN02g0DY5ECNn89+9rNs3bqVw4cP9+WwB6Uab4g3V71NybTidjfmAXhCETad8HBJQSJGJ1mVplCEvCS7ZF7EsJadYCXYw7rEs3MTuHhUIs/trqbcEyI1JZVZs2exenXPs8gQbSRi1TW2VzQTGCTd9oIRk1OeIFvLPaw/7uFArR+rDhlOK6kOyzlVxXn1b3/jhN/AOX42V4xP7fL4QNgkwWZIcxAhekAC5D6Wm2hHqei6MwAdjTtmZFHhCfH3A/XtPibVYXCyKUiDaeGmm27iqaee6sMRDz5KKd7ffZh9O7ZxdQcb8wA+ONZEyKTL5iDRzIvUPhbDW5LdQobLSlOgZ9nbr8zORtc0fr2xAoViyZLL2bB+A/UNDec0DrfNIKIUuyoHdrc9f9hkT2Uz7x9r5KNKH6GwItNlId1pwWac+0fvR3v38o8PN3FyzHyunhjdDNkVT8gkL0kq8AjRExIg9zGHRScvyU7jGR8ys/PcFGc5+fOuqnZbumqnd3LvrfRxyaKlHDt2jJ07d/blsAeVWm+IF156hWuvuoIEt7vD494pbSQnwcLEDEeHxwTCJm6b0e2OVkIMZWNS7d1qO32mDJeV26dnsumkhw+Oe0hJTmbO3LnntBa5RbLdgidgsrfKNyC77XlDEbaVN1PjDZPusJDusuDsRiDblYbGRv70xz+SPOcqLHYX103qOnsMYCpIkw6gQvSIBMj9ID/JRshUrR2iNDTumJlNvT/C/+2tafcxFl0jwWawvy7ErZ+/naeeempYdpjqjpVvv4sWCXW4MQ+gxhdiW3kzlxYkE+1f2D5PyCRfMi9CANEscqbb2qZkZXdcOymNMSl2/ntTBb6wyaJFi9i0cSP19fXnPJY0p4Uab3jAddvzBCNsPdmMUooUhyVmS7NMpXjm6aeZcd5cNvrTWDA2hQxn12uKfSGTFIeOyypf8oXoCQmQ+4HbZpDttuIJfpKJmZzhZF5+Aiv31FLvb3+XttMarY+cO3UuXp+PDRs29NWQB41TdY389fXV3LLss52WZPvb/jpMYMm4lE7PJ5kXIdoak2rvdtOQFhZN42tzc6j2hvnTzmqSk5I4f975vPX2W70aS5rToLwxyP5qH8EBsNyiwR9my0kPFp2Yr/ddvXoVoVAIb8FcghHFsilp3XqcN2SSl2iP6ViEGA4kQO4nI5Pt+D9VMumLJVn4wiZ/2d1+FhkgzWGhyhvm6lu+xB/+8AdMs/8/FAaS//3jSoomT6Jg9OgOj/GHTV77uI4LRiaQl9hxdtgfNkm2S+ZFiDOdaxZ5aqaLJeNS+L+9NZTWB1i4cBFbNm+hrv7cGyBpmkaGy0KFJ8SG402c8gT7LZtc4w2x9aQHpyX214wjpUdYs2YtN3/+C7x6oIE5eQmMSu466FVKoTRFqnzJF6LH4hogFxQUUFxcTElJCbNnzwagtraWxYsXM378eBYvXtzaHU4pxTe+8Q0KCwuZNm0aW7dujefQ+l2yw0KKQ29TNml0ip1F45L528e1nGoOdfjYNKcFZ/5E7ElpvP32230x3EFh34GDvL9zP9dfs7TT494+1EBT0OT6KemdHhddXiGZFyE+7VyyyAB3zMjEZdV5fGM5iYkJzLvgAt58s3dZ5JY9Gi6rzu5KLzsqmmnu4zJwlZ4g2yuaSbQb3epo1xNen48/PPUHbr75ZjbXGjQGItxY1Pm1q/WxIZMMpxV7jMckxHAQ93fN2rVr2b59O5s3bwbg4YcfZuHChRw4cICFCxfy8MMPA/D3v/+dAwcOcODAAZ588knuueeeeA+t3xWkOM76kPn8tEwA/rizqsPHGbpGkt1g7jW38vSfniUYHPotWLtimiaP/u8fWLx4EckJnbSMVor/21vDxHQHRZnODo9TSoGSzIsQ7TnXLHKKw8IdM7PYXelj9eFGFi1cyLatW6mu6fiuWXfZDJ1Ml5XmoMmG400crvUR7oMNfGUNAXae8pJi7111ivYoFM8++yxFU4uYWlzMX/fWMCnDQVFWx9euM/nCJiM6uUsmhOhYn3+tfPnll1m+fDkAy5cv56WXXmr9+e23346maZx//vnU19dTXl7e18PrU6lOC06r0aaeZ7bbyjUT0lh1qIHD9f4OH+uw6IwcNYrUwum8/Mrf+mK4A9oLL79Ko+bksgvmdnrchjIPJz0hrp+c3unmPG/IJN0lmRchOlKQ0vOKFgCXF6YwKcPBb7acQtmcXHTxRbz11psxG1ei3SDNaaG0IcjGsiZqvB3fjesNpRSl9X72VftId1qwGrGvk/7BB+upPHWKz372s/zjWBMVnhDLpmR0eu1qYSqFrmmkyJd8Ic5JXD/9NU1jyZIlzJo1iyeffBKAU6dOkZubC0BOTg6nTp0C4MSJE4wcObL1sfn5+Zw4cSKew+t3uqYxNtVO06e6U91cnEGS3eCx9eVEOllPl+KwcOGSq3jm5Tdobm6O93AHpKMV1dz///0Xf1y7lRtuvAG7pfO1fy9+VEOW28KFoxM7Pc4XMTtdnyzEcJfssJDu6nkWWUfj63NzaQxEWLGtkgULFrJzx06qqju+a9ZThq6R4Yw24dhW3syuU834zmFJSEeUUhyq9XOwxk+6s/sd8HqivKKcv73yCl+64w4sFgsrP6phRIKVeSO7bisN0Bw0yXJbz6kRiRAC4vrV8v333ycvL4/KykoWL17MpEmT2vxe07Qel8B58sknW4Ptqqoqqqp6d1FtWQPdX5Sp8Db6qfJpbS5kXy5y8Ydtlby8XWP+mI4bWeQkOxk140KeenYlN113ZV8Muf/nTCnq/RHe3rSbN99dT/Gk8dx/zTVYrdZON/wcrQ9wqrqW66ek09RQ3+FxplI0BUyCriBV3t5/uPT3fA1GMmc901/zlRgxOVzjJ93Zs01p6Rp8boyVdQfLuShb46KLL+att97myiuviPkYrUDpKZND5TAmyUK220JjJ+//rkRMxaGGEJXNYVIdOo2h2Aeg4XCYP//5Wa648krsdhubDlZQW1vHTVMzuj32Wl+ErAw7VVWxSZ7Ie7JnZL56bqDNWVwD5Ly8PACysrL47Gc/y8aNG8nOzqa8vJzc3FzKy8vJyspqPfb48eOtjy0rK2t9/Jnuvvtu7r77bgCmT59OZmZmr8cZi3P0RokjwIFaf5v1rgtTUnj3lGLFvmYuGJ9HTkLH9S6vu3whjzz6S66/5kpGZPfNa+mPOTOVos4XZmvpKZ5d+TINNZV8+babO2wl/Wm/3lWGz+JmafFIXJ0snWgKRBifbjAiq+MmIz3V339jg5HMWc/0x3xlAk1GM95gpMdlzW6Zk8ya8kP89iMfD182nx//x7+zaNEisk9/JsRSKtHAtiYQxh8yyHCZ5zRfYVOxt9JLwBJibE7sahx/2nPPPUd6ejqXXHIxGhp/23qckC2BxUUjcVi6fs6IqdCcJmPzEmOa3Zb3ZM/IfPXcQJqzuC2xaG5upqmpqfX/v/XWW0ydOpVrr72WFStWALBixQquu+46AK699lqefvpplFJs2LCB5OTk1qUYQ13W6eD3zI5QGhr3zc1B1+CxDeUoOl5qkZ2exqzZs3n498/j8wfiPt6+FjEVpzxBNhxv4vevvcfPf/4o4/Oz+N4/PdDt4LjCE+L9o01cMT6l0+AYIBBR5EjdUCG6Zcw5rkV2W3Xunp3NwVo/a8sCXHrZpbzxxhtxGGFUdNmFFRRsrwzw3tFGNpY1sb28mY+qvByu83GyMUBVc4haX5imQARvKEIwYmIqRTBisutUM9W+MBkua9yC423bt7Fv/35uvukmNDRK6wNsOunh2kmp3QqOAZqCEUYkWOOy9EOI4SJuGeRTpzcWQPR20a233srSpUs577zzuPHGG/nd737H6NGj+ctf/gLAlVdeyeuvv05hYSEul4unnnoqXkMbcGyGzuhkG8cag6Q5PvlPkumycseMbJ7YVMGqQw0s7qSpxfVXLOHJPz7HHf/8Y37yzbsYW9BxHeDBInw6MD5SF6CytpbXXlyJz9PI/fd+hfz8/B6d6+V9tegaXDep8+L6EVNh0SHZLrWPheiOM9ci9zSLfGlBIm8cdPGH7ZU8fvnFPPazn1BeUU5uTvySI06rTobLIMGqE1GKQNjEG1JETEVEgQloCkCBFv0fBWhadN9Iehw3vVXX1PD883/hnnvuweFwALDyoxrshsbVE7rXGAQgrCDT3XWXPSFEx+L2Th87diw7duw46+fp6emsXr36rJ9rmsYTTzwRr+EMeLmJdkrrA607j1tcNTGFdaUN/O+WU8zOSyDV0f5/MofDwX13Leftdzdw749+wV3XLeBz110TtyxHvJU1BDhc5ydsmuzespE3XnuV+QsWsHDhQixGzz6EPaEIbxys45LRSWS6Ov/Q8AQj5CbaJPMiRA+MSbGz6aSnxwGyhsbX5+Ty1VcP8cc9jVx33bU88fgTfOmOOxg3dmycRhtl6BoGGnRzyJ++NsdaY1MTT/3+9yxevLj1zliVN8S6Iw1cOSG121/aQxGFw4iWAhVCnDupYTVAOK06I5JsNAXa7gjX0bj//Fz8YZP/3lTR6Tk0NJZcMo+vfv0+Vv5jF//8r/8+4Ba9d8fR+gD7qn34Gmv502//h80bP+T+b97P5UuW9Dg4BnjjQD2+sOL6brRmDZmKLMm8CNEjyQ4LGU5LjytaAOQn2bhhSgarjzTgKpjGzbfcwm9+8xv+8cEHcRjpuYtncHzg4AEeeeQRJk+ezPz581t//tLeWkwF10/uXmMQgKZQhBFJtkGbHBFioJAAeQDJT7ITbKew/ahkO7cWZ/Du0SY2lDV1eZ6xeTnc97WvoecXcec3HuTDDz+Mx3DjoqwhwMfVzez88D0ee/RRioun8q1vfeucb7mGTcVL+2qZlu1ifFrnxfXDpsJm6CRK5kWIHhuT6sB7DmuRAW6amkFOgoXHN1YwcfIUHnjgAdauWcNzzz9POByO8UgHDlMp3nr7LZ566iluvfVWrr766tZA3BOK8PqBOi4uSOp0k/ZZ5zRVl3fKhBBdkwB5AEmwGWR2UFf0hqIMClLsPP5hBZ5Q11maRIeVz119OVd+8Rv8/MkV/OpXv8Lv77jxyEBQ3hRkc2kVz//ht+zasYMHvvUtFsxf0KvMzbvHGqn2hrl+ctfZ46ZAhBGJ1rhmioQYqlqyyOfS5tlh0fjq7ByONwb5y55qsrOy+Pa3v01DQwOPP/44jU1dJwYGG09zM//7P//D7t27+c4/fYcpkye3+f3rH9fhCyuWdePOV4tA2MRtM3D3cKmLEOJsEiAPMKNS7Ge1nwaw6hrfPD+XGl+Yp7ZWdutcNkNn1uRCln/rXznhg6/fdx8HDhyI9ZBjoqo5yKsffsRvH/8FYwtGc/83v9nrck8KxYsf1ZKfZGNOftfF9cNKkemW5iBCnKsxqQ6azzGLfH5+IpcVJPHMjmrePdqIw+Hgy1/+MoWFhfz85//JsTPKgA52R0qP8MgjPyM3N5dvfON+UlJS2vw+ZCpe3ldHSU7Xd77O5AmZ5CXJNUyIWJAAeYBJthskO4x2uz5NynDymUlpvHagnt2V3m6dz9A18tMSWLzsC1z0mc/z/X/5AX/5y18wzdh1leqtKk+AJ154kxf/vILbbrm5zW3G3thZ4eVgrZ/rJ6ehd9GaNRgxcVp0EmzylhDiXPUmiwzwwLwRFGU6+c9/nGB3pRdd07j66qu5/rPX8+snnmDz5s0xHnHfUijWrF3Dk0/+hmXLlvGZz3ym3X0Va480UOMLs2xK99ceAygF6U5ZXiFELEg0MMBomsbYVAfNHSyjWF6SSU6ChV+sP0kg0nFt5DPpmkamy0rBlBLu+pdH2LBpCw899BCVld3LRMfTsco6vvP/fsOhj3byz995kKIpU2J27hf31ZJsN1gwJqXLY5uCEUYm22VjixC9VNCLLLLd0Pi3y0aSnWDjh+uOc6whWte9pKSE+77xDV599VVeeuklTNW9a99A4vX5+O1vfsuWzVt48MEHmVY8rd3jTBQrP6phTIqdWSO636zIFzJJdug4rfKxLkQsyDtpAEp1WnBYdIKRsz9kHBadb5w/ghNNIZ7d1bM222lOC86kZK7/2neZOmM29913H+vWrYvRqHtu86693PPDn5OflcoD999HakpqzM59vDHIh2Uerp7QveL6CuJa31SI4SLFYSG9F1nkJLvBjxeMwqrrfH/1Map9IQDyRozg2//0T5SVlfHrX/+aZm/37qINBMeOH+eRn/2M5JRkvvnAN8lI7zgzvKnMw7GGIMuK0tG6uPN1Jm/IJE8aHAkRMxIgD0D66SxyQ6D9D5iZOW4WjU3mhT01HKrr2ca7ZLsFQ9cZfcFS/ukH/84zzzzDI488grcPP2yUUjz715f43n/9gc9ddzU3fLb924y98X97a7DqcM3EroNuf9gk0Wbgko0tQsREb9YiA+QkWPn3Bfk0BSP865rjrdUxEtxu7rn3XnJzc/n5z39OeUV5rIYcFwrF+++/z6+feIJrrr2WG2+4Eaul8yUQL3xUQ4bLwqWjk7r/PEqhNEWqfMkXImYkQB6gshKsuK0G/g4+ZO6elU2i3eCX68sJ9/B2o9tm4LboNLiy+f7Dj2K3O7jnnnvYs2dPLIbeqebmZv7txz/l/zbs4Ztfv4fZ09u/zdgb9f4wqw7Xs3BsCikdNFZpMybZ2CJETPU2iwwwPs3Jv1yST2ldgB+/U0b4dAlMQ9f53PXXs3TpUh775WPs2LkzVsOOKb/fz4o/rOC9997jgW99i1kzZ3b5mH3VPnZX+rh+chqWHjQr8oZMMpxW7Bb5SBciVuTdNEDpmsaEDCeNHWSRk+wG98zO4UCtn5f21vb4/HaLTqrDwpFGmP2ZL3L9l+7h33/yU1asWBG3uqMHDx7knvu+iS85n/vuvYf87My4PM+rH9cRjNCt0m5KKZRSpMnGFiFiKloXuXdrhWePSOAb5+eytbyZX24oR/HJ+ebOmcM9997LX/+6kv/5n//hwMEDbX7fnw4cPMDDP/sZVpuNB7/97W5X5Fm5pwa3VWfp+J4tN/OF5Uu+ELEm92MGsFSHQYbLQlMg0m7ziksKEllbmsDTO6q4YFQiIxJ6doG06BqZbguhiCJp9GRu++ef8sbKZ9nw7e/yvQe/wciRI2PyOpRS/P3vf+f3K55hwW33UFJS0uOWtN0ViChe/biOOXkJjEruej2eL2yS5rTikMyLEDEVzSIbHV6/umtpYQpVzSH+tKuaLLeV26d/8sV69KhR/OAHP2Djxk08++yzOB1O5i9YwIwZMzD0vn9P+/1+Xn7lFXbt2sVNN91I8dTiLh/jDZt8cKyJdaUNbD7ZzE1T03H14HrU0gI7uRt3y4QQ3SfvqAFM0zQK0518WNZEgtLPqrCgofG1OTnc/coh/mtDOT9dNKpHmzpaWA2NdKeFNEcqt975Fd79xwbu+uEv+MK1l3PDVYuwW87tw800TQ4ePMhf//pXDh89zhcf+gkp6elxC44B1hypp94f6Vb2GKIB8thUR9zGI8RwFr1+eXCfDuLO1eenZ1DpDfHnXdVkui1cUfhJhtVqsXLhBRcwb9489uzZzaq3V/G3V15h/vz5nH/++TgcffP+3rtvL88++ywTJkzke9/7Hi5nx/WLAxHFphMe1pU2sPFEE8EIZLkt3FiUzi1TM3r0vM1Bkyy3tUdLMoQQXZMAeYBLsBnkJ9mo8IRIbSdDkOmycufMbB7fWMFbBxu4vDDlnJ9L0zSS7BauXnAR0yaO5fd/ep51Ow/y1c/fwIQRaSTajC7LoAUCAbZv386HH37Ihg0bcLkTmHvhRVx6892EdQtJ9vj9yZmnG4OMTbUzPcfV5fHR5RUaKbKxRYi4SLAZFKTYONYQ7FWVGA2N++fmUusN86sNFaQ7LczJS2xzjK5pFE8tpnhqMUdKj7B61WreeOMNLrzoQi655FKSk7q/6a0nvD4fL774Ih9/vJ9bbrmFyZMmt3tcWCm2VzSz7kgjHxxrwhs2SbYbLBmXwvwxKUzOdHRZr709gYhJbqIsrxAi1iQyGARGpzg42RQiYiqMdrIEV05IYV1pA/+7+RTj0hwUpvU+YzIqbwTf/9bX+dvfXuUHDz/KZz63jBlTJjI6xU4gbOIJRgibioipqKmrZ+OW7WzavoN9Hx9kxMjRTCwq5voHriQtPRMNCGuQEsfgGGDLiWaONwb5pwtHdCuT7gmaZLkt2AxZXiFEvIxKtlPeFCIYMXv1XrPoGt+/NJ9/evMoP3n3BP+5ZDQT0tvP0o4pGMNdd91FZVUVa9eu5Sc/+THTp5ewcMECcnJyznkMn7Z7926ee/55pk6dyve++72zstUmio+qfKw70sD7x5qo90dwWXQuGJXIZWOSKMlxY+lFZt0fNnFaDVIcUoFHiFiTAHkQcFh0xqY6OFTnJ6OdLIyOxj9dmMeDb5byvdXH+PmS0d1af9sVq8XK9Z/9LFOmTOGPf/wjR0tmctnipTQ3+wkcPcRHH+1l//69VFVVMWF8IVOmzeKaGz9PUoIbi65haPRp442/7q0hw9n98kj+iElRcteZZiHEubMaOhMzHOw85SXT1bsvoy6Lzr8vyOeBN0r517XH+cXSMeQmdLzBNiszk5tuvJGrrryS995/j8f+678YPXo0c+ach3OKC4f93K6TnuZm/vrXv3Lk8GGWL7+d8YXj2/zeRLFyTw2vflxHZXMYmwFz8hKZPyaZ2SMSsBuxuS56ghGmZrmkwZEQcaApNQhbEp02ffp03njjjV6do6qqiszM+FRTiKWwqdhwvAmHReswC1PWFOTbb5ZiaBo/v7yg0w+Onmr2enn22WcpP3kSq9WK1+eluLiY4uJpFI4bh8XSv9+1Dtb5+fprR7hjRiY3FnW9hs8XMtH16C75eH+4DJa/sYFE5qxnBvp8KaXYUdFMc9Ds1Ya9FscaAnzrzVKS7QaPLh1DcjfPGQyF2LhxI9u2buVI6RHS0zMYOTKfUaNGM2rUKPLy8rDbOl+usG37Nl544QVmzZrNVVdddVaQHTYVv1hfzuojDZTkuFg0NoV5IxNxx7jDXSii8IVN5o1MbPfOYqwN9L+xgUbmq+f6a86uueaadtvYSwZ5kLDoGuPTHew+5SPT3f6FNj/Rxk8XjeI7bx3lu6tK+c8lBWS6YhMku10u7rzzDvbt24dhWBg/vvCcNgTGwylPiKe2VuKwaFzZzfJInqBJSa5kXoToC5qmMT5GG/Ygumzjh5eN5LurjvLDtcf46aLR3apEY7NauejCCykqmkJiQiLlFeUcO3ac48eOsXHjh5SXV5CZmXE6YB7JqFGjycsbgdVipbGpib88/zzl5eXcddeXGTtmzFnn94dNfvLuCTad9HD79AxuKc6I23WyIRhhYrqjT4JjIYYjCZAHkUy3lUR7AF/IxNlBNmJMioMfLxjFP686yndXRZdbdKdZRndoaEyeNJm6+rp+D47DKroL/PWP69l00gPAl0oyu1UhIxgxcVo16TolRB9yt2zYawySFoNr0tQsF9+5MI+fvHeCe189zH3n5zIjx93tx1ssFkbmj2Rk/ki44AIAQuEQJ0+Wc/zYMY4eO8YH//iAU5WVZGdl0dDYyPnnn8/yLy5vtxteYyDCv649xv5qP9+Ym9PtL+vnImIqdCDLLfXbhYgXiRAGEf10FmbLSU+HATLAxAwn/7FgFN9bfYzvrjrGI0tGkzhE2ihXeUO8ebCeNw7WU+0Nk+awcMvUDJYWppDdzSUlTcEIkzJcvc5iCSF6ZmSynZMx2LDX4uLRSTxsN/ivDeV8d9UxFo1J5q5ZWeecFLBarIweNYrRo0Zx0emfBUMhTp44gd1hJzcnt93HVTaH+P7qY1R4gnz/knwuGpXY7nGxUh8IU5BixyobjIWIGwmQB5lUp4Ust7XL4vtTs1z866X5/HDdcX6w5hj/36LRPSo+P5BElGLLSQ+vH6jnwzIPCpiV6+ae83KYm5fQo/qfYVNhaBqZknkRos+1btir8Ha4VKynSnLc/PrqcTy3u5oX9lTz4UkPd8/MYtG45Jjc6bJZrRQUFHT4+2MNAb63+hjeoMmPF4xieg+y2OfCVAqlIDex9xuxhRAdkwB5EBqX5mBDN9byzR6RwHcvzucn75Txw7XH+ff5o3BYBk/WtMYX4q1D9fz9QD2VzWFSHAY3FKVzxfjUc96A2BCIUJjmkKL6QvSTDJeV9E46hJ4Lh0XjiyWZXFaQxC83nOT/nd4k9/W5ueTHsUbw3mof/7rmGIau8cjloynsg6ZDjYEIeUl26f4pRJzJO2wQctsMRibZqA9Eujz2wpGJfPvCEew85eUn75YRMgd+0ZLS+gD/8c5xbn/xICu2VzMi0cb3Ls7jmevHc8eMrHMOjiOnX7us2xOi/7Rs2POHzdb3ZKwUpNh5dGkBX5+Tw8fVfu752yGe210dl+vephMe/vntoyTYDB5dWtAnwbFSipCpyE+SxiBCxJtkkAepUSl2TnqCHTYPOdOCMcn4wyb/9WEFj7x/gocuzutVcfp42lvt419WH0PX4DOT0rhiQmrMMkCNwQijkm3YJfMiRL9y2wzGpNo52ssOe+3R0bh6Qirnj0zgfzad4g/bq1h7pIFvnD+CosyO2z/3xJojDfy/D04yOsXOTxaOarfLaTx4gibZbivuIbKnRIiBTCKFQcph0RmX6qDe33UWGeDK8al8eVYW7x1r4pfryzEZeJnkXae8fHfVUZLsBo9fNZYvz8qOWXCslCIcUdKSVYgBYmSyHZuuEQibcTl/htPKv1ySzw8vy6c5ZPLgm6U8/mE5nlD3rpkdeXFvDY/84yRFWS4eWTK6z4JjAH9EMTIGTaCEEF2TDPIglpto41h9oNs7wj83OR1fyOSPO6txWDS+Nien38u1tdhS7uFH68rIclt5ePEoMpyxXQbRFIyQm2jDZZXMixADgdXQmdCyYS+Od3XOz09kWo6bp7dX8vK+Oj447mH5JAeTNRdpLgtuq96t66BC8fttVbywp4YLRybwnYvyY9YRrzu8oQgpDp3kPgzIhRjO5J02iLU0D9l1qvs7wm+bloE3ZPLi3lpcVoM7ZmTFeZRd21DWxE/eLSM/yc5PF42KWd3mMwUk8yLEgJPhspLRjao8veWy6Hx1dg4LxiTzyw3lPLOjiurt0frpNkMjzWnp+B+XhVSHhd9vq+TtQw1cOT6Fr83JwejjZWrekMn09PhWyBBCfEIC5EEu020lyWHgDUW6lR3V0PjyrCwCYZO/7KkhGDG5vSSr30rAvX+siZ++V8bYVAc/WTiKpDh8SDYHI6Q7LXH9ABZC9JymaYxPc7ChrAmXqce9K9yEdCf/deUYth2uoEl3UesLU+sNU+sPU+sLU1ofYOvJZrwdLPu4tTiDL0yPX3e8jgTCJk6rIc2NhOhD8m4b5Fp2hG8+6en28gENja/NzUEDXtpXx7rSRr4wPZPLC1P6dPPemiMN/PwfJ5mY4eA/Fo4iIU7LH3xhk0mZrricWwjRO67TG/ZK62O/Ya89Fk2jMN1Bakpyh8f4w2Y0aPZGA+daX5icBCvn58e3AUhHmkIRijKluZEQfUkC5CEgxWEh22WlIRAmyd69/6Q6Gl+fm8uicSn8ZsspfvVhBS/vq+WumVmcl5cQ9wzJGwfr+OWGCqZnu/jh/JE445TB9odNEmwGKQ7JHgsxUI1MdnCyMUQgbA6IKjMOi86IBBsjEvp/U28oorDpOhkuKU8pRF/q/yuRiIlxaQ6CkWiXpZ6YlOHk55eP5geX5hOOKP51bRnfXXWMg3X+OI0UXtkfDY5n5br50fxRcQuOIVoWaUyqHU0yL0IMWBZdY2KGg8Zg7ypMDEWNwQgFKfa4Lz8RQrQlAfIQ4bIZFKTYqPGFe/xYDY0LRybyv9eO46uzszlU6+frrx3h5x+cpNobiuk4V35Uw683VXB+fgL/dtnIuHb2C0ZMHBaNdMm8CDHgpbusZLisNAZ6fg0bqlqbG51jcyQhxLmTAHkIKUh1kOmyUuc/tw8Yq67xmUlpPPWZQpZNSWNdaQN3vHyIFdurOty00l0KxZ92VfHbrZVcPDqRf7kkH1ucSyQ1BUwKUu2ybk+IQaBlw17IVIQiA69Oe39oCEYYnWzrVhlPIURsybtuCNE1jUmZTqy6RnMvblUm2AzumpnNb68tZN7IRJ7dXc0dLx3k7wfrCPdwCQdEg+M/bK/imR3VLBqTzEMX5WGJ8+3CsKnQdchy9/8aQiFE97hsBlOzXNT5wzFvQz3YmEphmorcRClPKUR/kE16Q4zN0Jme4+bDE01YI1qvMg85CVa+e1Een52UxpNbTvHYhgpe2ltLSYqJ5gxgNXTshoZV17BbdGxG9Pmi/6thO/2z94428vK+Oq4oTOG+83PQ+6BEUmMgwphUR9wDcSFEbGW6bUzMUOyv9pHpsgzb/QNNgQgjkmw4rZLHEqI/SIA8BLltBtOyXGyv8JLh0nq9xGBShpP/d/lo/nHMw7O7qtl5ykNlxCQQMenuyovrJqXy1dnZfVI/1FQKE0WOrNsTYlDKT7LhD5scbwzEvKvmYBE0FflJkj0Wor9IgDxEZbhtFKabHKr1kxmDTWoaGheNSuSiUYnU1deRmpIKQERF1wsGIopQxDz9v4pA2CRkKvwRE4ehU5Tl7LPi+o2BCCOT7AOiXJQQouc0TWNcmgNfyKTWFyZtmDXI8AQjZLqsJNikPKUQ/WV4XXWGmdHJdpoCEWq88fuAMTQNw6IR7Q7d/xdzpRQhE/ISZe2xEIOZrmlMznSyvbyZxh7UeB8KvCGTKVnS3EiI/iQptiFM0zQmZThxWPRebdobTDxBk+wECy7JvAgx6FkNneIcNxoa3tDwuIb5QibJDoNku1zDhOhPEiAPcVZDZ1q2i4CpCEZ6V6ptMPCFTUYly7o9IYYKh0Vneq4bXzi6dGuoaw5FGJvqGLabE4UYKCRAHgZcNoPiTBf1gciQLp3UHIyQ7rIMq1uxQgwHCTaD6TluGoMRwkP4GuYNRXBYdFKH2ZprIQYiCZCHiXS3lfFpTmrPodPeYOENmYxOcfT3MIQQcZDmtDAl00WNL4x5DvXYB6pAOLoRsdobxtCj666luZEQ/U++pg4jo5JteIIRqppDQ2ZXeDBi0hSMYCrIcFlIdci6PSGGqtzEaPm3w7V+MgZxjeSW65YC3FaDwnQHaU4LLqtcv4QYKIZGlCS6RdM0JmY4aQ5GaApESBykm0ACYRNPKBoUu6wGhWlO0pwW3LIxT4ghryDFji9sUuEJkTGIvui3BsUKnKevW+lO2VAsxEA1eK4uIiYsusbUbBebT3gIhM1BUyu4JShuzbhIUCzEsKRpGhPTnQTDJvX+MCmOgfsxFoyYeEImplI4DF2uW0IMIgP3yiLixmU1KM52s/WkhzRdwxig7Zg/HRSPP/3hIhkXIYY3Q9eYkuVi68lmPMHIgGyoUecPY2gaY1IcpLssuK36oF0SIsRwJAHyMJXqtDAxw8m+ah/pTsuACpJDEUWdP0yiXYJiIUT7bIbOtJzo3TB/2MQxgO6G1fjCpDgMirJc2IyBMy4hRPdJgDyM5SXZiCg4WOvDbdUHxAYRf9jEEzSZlu0iK0G64QkhOuayGpTkutlZ4cUbCpPqMPo1S6uUotoXJifBxsQMJ5YBlHgQQvSMfLUdxjRNY3SKnfPyElBo1PrDqH4sn9QcjOALm8wc4ZbgWAjRLUl2C3PyE8hNtFLlDePvp2YiETMaHI9KtjM5U4JjIQY7CZAFSXYL5+UlkJtgpcoX7peOew2BMAqYPSJhQG+6EUIMPDZDZ2KGi1kjEgibqs9rJYdPB8fj0hwUpjmkjrEQQ4AEyAKIVreYmOFiRrYbb8ikPtB3DUVqfWGcVp2ZIxJkd7cQ4pylOi3MyU9kVLKNGm8YbygS9+cMRkzqfGGmZrkoSJEW0UIMFRIgizbS3Vbm5CeSardQ2RyKa1vX6Hq9EOkuCyU5CQNqk40QYnCy6Brj0pycl5+ApmlUe0NE4nQd84dNGgMRSnLc5CbKsjAhhhKJSMRZHBadqdkupmQ5afCH8QRjn4WJmIoqb5iRSXaKslyyXk8IEVNJdguzRyQwLs1JrS/217GWPROzRiSQ7rbG9NxCiP4nAbJol6ZpjEi0Myc/EZslmoWJ1Zq+UCS6RnBihlPW6wkh4sbQoxuR546MXseqvLG5K9YUiLTumUiWPRNCDEkSIItOuW0GM3ISKEixUxODHeL+sElDIMK0bBcjk+2yXk8IEXcJNoOZuQlMTHdSHwjTFDz361idP4zV0JiRK3smhBjK5Kuv6JKha4xNc5LmsvJRpZcaXwTNEcaqa9gMHavRvSC3ORghaCpmjnBLpQohRJ/SNY38ZDtpLgubDnqo9oXRAJSC05cwi6Zh0T/559MNlGp80VrLU6QBiBBDnkQpottSHNFycIfwYk+00RyMblBpDCpAgdJAA6sOVl3HZnzyAdMQiLZdnS2VKoQQ/chlNZiSYSctPYmQqQhFFMGISTBi4g+b+EImzSETT8gkZKrWIDqiIDfRxqQM54DqPCqEiI+4B8iRSITZs2eTl5fHq6++ypEjR7j55pupqalh1qxZPPPMM9hsNgKBALfffjtbtmwhPT2d559/noKCgngPT/SQ1dBJdxpkpjpbf2YqhT9sEowoAmETTzBCU8CkORQhEI4Gz0kOg6lZbqlUIYQYEIzTGeLozaz2v7RHTEUwogib0X+SHYbsmRBimIh7tPLYY48xefLk1n9/6KGHeOCBBzh48CCpqan87ne/A+B3v/sdqampHDx4kAceeICHHnoo3kMTMaJrGi6rQYrDQnaCjXFpTkpy3Vw4KolLCpI4Pz+RGVLGTQgxyBi6htOqk2g3SHVaJDgWYhiJa8RSVlbGa6+9xl133QVE696uWbOGZcuWAbB8+XJeeuklAF5++WWWL18OwLJly1i9enW/tj0WsWHRNVw2Q25JCiGEEGLQiOsSi29+85s88sgjNDU1AVBTU0NKSgoWS/Rp8/PzOXHiBAAnTpxg5MiR0UFZLCQnJ1NTU0NGRkabcz755JM8+eSTAFRVVVFVVdWrMdbV1fXq8cORzFnPyHz1nMxZz8h89YzMV8/JnPWMzFfPDbQ5i1uA/Oqrr5KVlcWsWbNYt25dzM579913c/fddwMwffp0MjMze33OWJxjuJE56xmZr56TOesZma+ekfnqOZmznpH56rmBNGdxC5D/8Y9/8Morr/D666/j9/tpbGzk/vvvp76+nnA4jMVioaysjLy8PADy8vI4fvw4+fn5hMNhGhoaSE9Pj9fwhBBCCCGEaFfc1iD/9Kc/paysjNLSUp577jkWLFjAn/70J+bPn8/KlSsBWLFiBddddx0A1157LStWrABg5cqVLFiwQJpICCGEEEKIPtfnZQV+9rOf8eijj1JYWEhNTQ133nknAHfeeSc1NTUUFhby6KOP8vDDD/f10IQQQgghhOibRiGXXXYZl112GQBjx45l48aNZx3jcDh44YUX+mI4QgghhBBCdEgK0wohhBBCCHEGCZCFEEIIIYQ4gwTIQgghhBBCnEECZCGEEEIIIc4gAbIQQgghhBBnkABZCCGEEEKIM0iALIQQQgghxBkkQBZCCCGEEOIMmlJK9fcgzlVGRgYFBQW9OkdVVRWZmZmxGdAwIXPWMzJfPSdz1jMyXz0j89VzMmc9I/PVc/01Z6WlpVRXV5/180EdIMfC7Nmz2bx5c38PY1CROesZma+ekznrGZmvnpH56jmZs56R+eq5gTZnssRCCCGEEEKIM0iALIQQQgghxBmGfYB899139/cQBh2Zs56R+eo5mbOekfnqGZmvnpM56xmZr54baHM27NcgCyGEEEIIcaZhn0EWQgghhBDiTBIgCyGEEEIIcYZBFSC/8cYbTJw4kcLCQh5++OHWnz/++OMUFhaiaVq7texa3HnnnUyfPp1p06axbNkyPB4PAIFAgJtuuonCwkLmzp1LaWlpu49fsWIF48ePZ/z48axYsaL150uXLmX69OkUFRXx1a9+lUgkEpsX3EsDdb6ef/55pk2bRlFREQ899FBsXmyM9PecLV26lJSUFK6++uo2P//iF7/ImDFjKCkpoaSkhO3bt/f6tcZCf87X9u3bmTdvHkVFRUybNo3nn3++x8/fH+I1Z++++y4zZ87EYrGwcuXKHj9/R+ftbwN1vtasWcPMmTOZOnUqy5cvJxwOx+DV9l5/z9cdd9xBVlYWU6dObfPzH/7wh+Tl5bVew15//fVevtLY6c85O378OPPnz2fKlCkUFRXx2GOPtf7uhRdeoKioCF3XB1T5s47m67bbbmPixIlMnTqVO+64g1Ao1O7jjxw5wty5cyksLOSmm24iGAwCA/AapgaJcDisxo4dqw4dOqQCgYCaNm2a2rNnj1JKqa1bt6ojR46o0aNHq6qqqg7P0dDQ0Pr/H3jgAfXTn/5UKaXUE088ob7yla8opZR69tln1Y033njWY2tqatSYMWNUTU2Nqq2tVWPGjFG1tbVtzmuaprr++uvVs88+G5sX3QsDdb6qq6vVyJEjVWVlpVJKqdtvv12tWrUqZq+7N/p7zpRSatWqVeqVV15RV111VZufL1++XL3wwgu9en2x1t/ztX//fvXxxx8rpZQ6ceKEysnJUXV1dT16/r4Wzzk7cuSI2rFjh/rCF77Q4d9KZ8/f0Xn700Cdr0gkovLz89X+/fuVUkr94Ac/UL/97W9j9bLPWX/Pl1JKvfPOO2rLli2qqKiozc//7d/+Tf3nf/5nb15eXPT3nJ08eVJt2bJFKaVUY2OjGj9+fOvzf/TRR2rfvn3q0ksvVZs2bYrJ6+2tzubrtddeU6ZpKtM01c0336x+/etft3uOG264oTVO+spXvtJ63EC7hg2aDPLGjRspLCxk7Nix2Gw2br75Zl5++WUAZsyY0a2OeklJSQAopfD5fGiaBsDLL7/M8uXLAVi2bBmrV69GfWrv4ptvvsnixYtJS0sjNTWVxYsX88Ybb7Q5bzgcJhgMtp63Pw3U+Tp8+DDjx49v7ZazaNEi/vrXv8bqZfdKf88ZwMKFC0lMTIzRK4qv/p6vCRMmMH78eABGjBhBVlYWVVVVPXr+vhbPOSsoKGDatGnoeseX9c6ev6Pz9qeBOl81NTXYbDYmTJgAwOLFiwfEday/5wvgkksuIS0trXcvpA/195zl5uYyc+ZMABITE5k8eTInTpwAYPLkyUycOLE3Ly/mOpuvK6+8Ek3T0DSNOXPmUFZWdtbjlVKsWbOGZcuWAbB8+XJeeuklYOBdwwZNgHzixAlGjhzZ+u/5+fmtf0Q98aUvfYmcnBz27dvHfffdd9a5LRYLycnJ1NTU9Oj5L7/8crKyskhMTGz9D9+fBup8FRYWsn//fkpLSwmHw7z00kscP378XF5izPX3nHXl+9//PtOmTeOBBx4gEAj0eFyxNpDma+PGjQSDQcaNG9fj5+9L8ZyzWDz/uZ43XgbqfGVkZBAOh1tve69cuXJAXMf6e7668vjjjzNt2jTuuOMO6urqYnbe3hhIc1ZaWsq2bduYO3fuOT2+L3RnvkKhEM888wxLly496/E1NTWkpKRgsVg6fHxvnj+Wf7uDJkCOlaeeeoqTJ08yefLkNmsWe+vNN9+kvLycQCDAmjVrYnbe/hbr+UpNTeW///u/uemmm7j44ospKCjAMIwYjHTgiMff2E9/+lP27dvHpk2bqK2t5Wc/+1lMzjsQ9Ha+ysvL+cIXvsBTTz3VZXZrqIjXdSxe5+1vsX5dmqbx3HPP8cADDzBnzhwSExOH1HUsHn8H99xzD4cOHWL79u3k5uby4IMPxuS8A0Vv58zj8fC5z32OX/7yl62Z0MHq3nvv5ZJLLuHiiy/u8+eO5d/uoPk0ycvLa/MNvaysjLy8vE4fc/nll1NSUsJdd93V5ueGYXDzzTe33hI789zhcJiGhgbS09N7/PwOh4PrrruuNd3fnwbyfF1zzTV8+OGHrF+/nokTJ7bepuxv/T1nncnNzUXTNOx2O1/60pfYuHFjtx8bLwNhvhobG7nqqqv4yU9+wvnnn9/blxR38ZyzWD3/uZw3XgbyfM2bN4/33nuPjRs3cskllwyI61h/z1dnsrOzMQwDXdf58pe/PCCuYTAw5iwUCvG5z32O2267jeuvv75Hj+1rXc3Xj370I6qqqnj00Udbf3bmfKWnp1NfX9+6qbU7892T54cY/u32agVzHwqFQmrMmDHq8OHDrQuzd+/e3eaYzhbSm6apDhw40Pr/H3zwQfXggw8qpZR6/PHH22wIuuGGG856fE1NjSooKFC1tbWqtrZWFRQUqJqaGtXU1KROnjzZOsYbb7xR/epXv4rZ6z5XA3W+lFLq1KlTSimlamtr1fTp01s3uvS3/p6zFmvXrj1rk17L35hpmur+++9XDz300Lm9yBjq7/kKBAJqwYIF6he/+EWHYxxom/TiOWctOtvQ2dHzd+e8/WGgzpdSn1zH/H6/WrBggVq9enWvXmss9Pd8tThy5MhZm/RarmFKKfXoo4+qm266qduvK576e85M01Rf+MIX1P3339/hGAfSJr3O5us3v/mNmjdvnvJ6vZ2eY9myZW026T3xxBNtfj9QrmGDJkBWKrpDcvz48Wrs2LHqxz/+cevPH3vsMZWXl6cMw1C5ubnqzjvvPOuxkUhEXXDBBWrq1KmqqKhI3Xrrra07Hn0+n1q2bJkaN26cOu+889ShQ4faff7f/e53aty4cWrcuHHq97//vVJKqYqKCjV79mxVXFysioqK1Ne//nUVCoXi8Op7biDOl1JK3XzzzWry5Mlq8uTJA6Lix5n6e84uuugilZGRoRwOh8rLy1NvvPGGUkqp+fPnt573tttuU01NTXF49T3Xn/P1zDPPKIvFoqZPn976z7Zt27r9/P0lXnO2ceNGlZeXp1wul0pLS1NTpkzp9vN3dt7+NhDnSymlvv3tb6tJkyapCRMmdPolra/193zdfPPNKicnR1ksFpWXl9da3ePzn/+8mjp1qiouLlbXXHNNm4C5v/XnnL333nsKUMXFxa3Xsddee00ppdSLL76o8vLylM1mU1lZWWrJkiVxmoGe6Wi+DMNQY8eObX0dP/rRj9p9/KFDh9R5552nxo0bp5YtW6b8fr9SauBdw6TVtBBCCCGEEGcYNGuQhRBCCCGE6AsSIAshhBBCCHEGCZCFEEIIIYQ4gwTIQgghhBBCnEECZCGEEEIIIc4gAbIQQgwSNTU1lJSUUFJSQk5ODnl5eZSUlJCQkMC9997b38MTQoghQ8q8CSHEIPTDH/6QhIQEvv3tb/f3UIQQYsiRDLIQQgxy69at4+qrrwaigfPy5cu5+OKLGT16NC+++CLf+c53KC4uZunSpYRCIQC2bNnCpZdeyqxZs7j88sspLy/vz5cghBADigTIQggxxBw6dIg1a9bwyiuv8PnPf5758+eza9cunE4nr732GqFQiPvuu4+VK1eyZcsW7rjjDr7//e/397CFEGLAsPT3AIQQQsTWFVdcgdVqpbi4mEgkwtKlSwEoLi6mtLSU/fv3s3v3bhYvXgxAJBIhNze3P4cshBADigTIQggxxNjtdgB0XcdqtaJpWuu/h8NhlFIUFRWxfv36/hymEEIMWLLEQgghhpmJEydSVVXVGiCHQiH27NnTz6MSQoiBQwJkIYQYZmw2GytXruShhx5i+vTplJSU8MEHH/T3sIQQYsCQMm9CCCGEEEKcQTLIQgghhBBCnEECZCGEEEIIIc4gAbIQQgghhBBnkABZCCGEEEKIM0iALIQQQgghxBkkQBZCCCGEEOIMEiALIYQQQghxhv8fmiV9bv9gE7YAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the groud truth, actual forecast and confidence interval \n", + "fig, ax = model2.plot_forecast(time_series=test_data, plot_forecast_uncertainty=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train a partial autosarima model\n", + "\n", + "Here, the user has pre-defined the AR, MA, Seasonal AR, and Seasonal MA hyper-parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:merlion.models.automl.seasonality:Automatically detect the periodicity is [24]\n", + "INFO:merlion.models.automl.autosarima:Seasonal difference order is 1\n", + "INFO:merlion.models.automl.autosarima:Difference order is 0\n" + ] + } + ], + "source": [ + "# Specify the configuration of partial AutoSarima \n", + "# We explicitly specify values of p, q, P, Q in the order and seasonal order,\n", + "# and we set auto_pqPQ=False.\n", + "# Because auto_d=True, auto_D=True, and auto_seasonality=True by default, we\n", + "# can specify arbitrary values for them in the order and seasonal order (e.g. \"auto\")\n", + "config3 = AutoSarimaConfig(auto_pqPQ=False, order=(15, \"auto\", 5),\n", + " seasonal_order=(2, \"auto\", 1, \"auto\"), maxiter=5)\n", + "model3 = AutoSarima(config3)\n", + "\n", + "# Model training\n", + "train_pred, train_err = model3.train(\n", + " train_data, train_config={\"enforce_stationarity\": True,\"enforce_invertibility\": True})" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial AutoSarima without approximation sMAPE is 3.5288\n" + ] + } + ], + "source": [ + "# Model forecasting\n", + "forecast3, stderr3 = model3.forecast(len(test_data))\n", + "\n", + "# Model evaluation\n", + "smape3 = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=forecast3)\n", + "print(f\"Partial AutoSarima without approximation sMAPE is {smape3:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the groud truth, actual forecast and confidence interval \n", + "fig, ax = model3.plot_forecast(time_series=test_data, plot_forecast_uncertainty=True)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/v2.0.2/tutorials/advanced/2_ForecastInvertPOC.html b/v2.0.2/tutorials/advanced/2_ForecastInvertPOC.html new file mode 100644 index 000000000..6be8500e4 --- /dev/null +++ b/v2.0.2/tutorials/advanced/2_ForecastInvertPOC.html @@ -0,0 +1,981 @@ + + + + + + Proof of Concept: Inverse Transforms for Forecasters — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

Proof of Concept: Inverse Transforms for Forecasters

+
+
[1]:
+
+
+
import matplotlib.pyplot as plt
+
+from merlion.utils import TimeSeries
+from ts_datasets.forecast import M4
+
+ts, md = M4(subset="Hourly")[2]
+train = TimeSeries.from_pd(ts[md["trainval"]])
+test = TimeSeries.from_pd(ts[~md["trainval"]])
+
+ax = plt.figure(figsize=(10, 6)).add_subplot(111)
+ax.plot(ts)
+ax.axvline(train.to_pd().index[-1], ls="--", c="k")
+plt.show()
+
+
+
+
+
+
+
+
+100%|██████████| 414/414 [00:00<00:00, 861.84it/s]
+
+
+
+
+
+
+../../_images/tutorials_advanced_2_ForecastInvertPOC_1_1.png +
+
+
+
[2]:
+
+
+
import matplotlib.pyplot as plt
+import pandas as pd
+
+from merlion.evaluate.forecast import ForecastMetric
+from merlion.models.forecast.base import ForecasterBase
+from merlion.models.forecast.prophet import Prophet, ProphetConfig
+from merlion.transform.resample import TemporalResample
+from merlion.transform.sequence import TransformSequence
+from merlion.utils import TimeSeries
+
+def get_model(transform=None):
+    if transform is not None:
+        transform = TransformSequence([TemporalResample(), transform])
+    prophet = Prophet(ProphetConfig(add_seasonality="auto", transform=transform))
+    return prophet
+
+def eval_model(model: ForecasterBase, train_data: TimeSeries, test_data: TimeSeries,
+               apply_inverse=True):
+    og_train = train_data
+    model.config.invert_transform = apply_inverse
+    yhat_train, _ = model.train(train_data)
+    if not apply_inverse:
+        train_data = model.transform(train_data)
+
+    t = test_data.time_stamps
+    yhat_test, _ = model.forecast(t)
+    if not apply_inverse:
+        test_data = model.transform(og_train + test_data).align(reference=t)
+
+    print(f"Train sMAPE: {ForecastMetric.sMAPE.value(train_data, yhat_train):.2f}")
+    print(f"Test  sMAPE: {ForecastMetric.sMAPE.value(test_data, yhat_test):.2f}")
+
+    ax = plt.figure(figsize=(10, 6)).add_subplot(111)
+    ax.plot((train_data + test_data).to_pd(), label="true")
+    ax.plot((yhat_train + yhat_test).to_pd(), label="model")
+    ax.axvline(pd.to_datetime(t[0], unit="s"), c="k", ls="--")
+    ax.legend()
+    plt.show()
+    return yhat_test
+
+
+
+
+
[3]:
+
+
+
print("No transform...")
+base = eval_model(get_model(), train, test, apply_inverse=True)
+
+
+
+
+
+
+
+
+21:14:26 - cmdstanpy - INFO - Chain [1] start processing
+21:14:26 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+No transform...
+Train sMAPE: 4.88
+Test  sMAPE: 17.58
+
+
+
+
+
+
+../../_images/tutorials_advanced_2_ForecastInvertPOC_3_2.png +
+
+
+
[4]:
+
+
+
from merlion.transform.normalize import MeanVarNormalize, MinMaxNormalize
+
+print("Normalize...")
+eval_model(get_model(MeanVarNormalize()), train, test, apply_inverse=False)
+
+print("Normalize + invert...")
+norm = eval_model(get_model(MeanVarNormalize()), train, test, apply_inverse=True)
+
+
+
+
+
+
+
+
+Normalize...
+
+
+
+
+
+
+
+21:14:26 - cmdstanpy - INFO - Chain [1] start processing
+21:14:26 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+Train sMAPE: 54.41
+Test  sMAPE: 118.27
+
+
+
+
+
+
+../../_images/tutorials_advanced_2_ForecastInvertPOC_4_3.png +
+
+
+
+
+
+
+Normalize + invert...
+
+
+
+
+
+
+
+21:14:27 - cmdstanpy - INFO - Chain [1] start processing
+21:14:27 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+Train sMAPE: 5.73
+Test  sMAPE: 17.55
+
+
+
+
+
+
+../../_images/tutorials_advanced_2_ForecastInvertPOC_4_7.png +
+
+
+
[5]:
+
+
+
from merlion.transform.normalize import BoxCoxTransform
+
+print("Box-Cox transform...")
+eval_model(get_model(BoxCoxTransform()), train, test, apply_inverse=False)
+
+print("Box-Cox transform + invert...")
+boxcox = eval_model(get_model(BoxCoxTransform()), train, test, apply_inverse=True)
+
+
+
+
+
+
+
+
+Box-Cox transform...
+
+
+
+
+
+
+
+21:14:27 - cmdstanpy - INFO - Chain [1] start processing
+21:14:27 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+Train sMAPE: 0.99
+Test  sMAPE: 3.36
+
+
+
+
+
+
+../../_images/tutorials_advanced_2_ForecastInvertPOC_5_3.png +
+
+
+
+
+
+
+Box-Cox transform + invert...
+
+
+
+
+
+
+
+21:14:28 - cmdstanpy - INFO - Chain [1] start processing
+21:14:28 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+Train sMAPE: 3.61
+Test  sMAPE: 12.30
+
+
+
+
+
+
+../../_images/tutorials_advanced_2_ForecastInvertPOC_5_7.png +
+
+
+
[6]:
+
+
+
from merlion.transform.moving_average import MovingAverage
+
+print("Moving Average...")
+eval_model(get_model(MovingAverage(n_steps=5)), train, test, apply_inverse=False)
+
+print("Moving Average + invert...")
+ma = eval_model(get_model(MovingAverage(n_steps=5)), train, test, apply_inverse=True)
+
+
+
+
+
+
+
+
+Moving Average...
+
+
+
+
+
+
+
+21:14:29 - cmdstanpy - INFO - Chain [1] start processing
+21:14:29 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+Train sMAPE: 4.46
+Test  sMAPE: 17.09
+
+
+
+
+
+
+../../_images/tutorials_advanced_2_ForecastInvertPOC_6_3.png +
+
+
+
+
+
+
+Moving Average + invert...
+
+
+
+
+
+
+
+21:14:29 - cmdstanpy - INFO - Chain [1] start processing
+21:14:29 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+Train sMAPE: 5.49
+Test  sMAPE: 17.88
+
+
+
+
+
+
+../../_images/tutorials_advanced_2_ForecastInvertPOC_6_7.png +
+
+
+
[7]:
+
+
+
from merlion.transform.moving_average import DifferenceTransform
+
+print("Difference transform...")
+eval_model(get_model(DifferenceTransform()), train, test, apply_inverse=False)
+
+print("Difference transform + invert...")
+diff = eval_model(get_model(DifferenceTransform()), train, test, apply_inverse=True)
+
+
+
+
+
+
+
+
+Difference transform...
+
+
+
+
+
+
+
+21:14:30 - cmdstanpy - INFO - Chain [1] start processing
+21:14:30 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+
+Train sMAPE: 53.17
+Test  sMAPE: 48.12
+
+
+
+
+
+
+../../_images/tutorials_advanced_2_ForecastInvertPOC_7_3.png +
+
+
+
+
+
+
+Difference transform + invert...
+
+
+
+
+
+
+
+21:14:30 - cmdstanpy - INFO - Chain [1] start processing
+21:14:30 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+Train sMAPE: 6.47
+Test  sMAPE: 19.18
+
+
+
+
+
+
+../../_images/tutorials_advanced_2_ForecastInvertPOC_7_7.png +
+
+
+
[8]:
+
+
+
fig = plt.figure(figsize=(10, 6))
+ax = fig.add_subplot(111)
+ax.plot(test.to_pd(), label="true")
+series = [("original", base), ("norm", norm), ("box-cox", boxcox), ("ma", ma), ("diff", diff)]
+smapes = {name: ForecastMetric.sMAPE.value(test, ts) for name, ts in series}
+
+for name, ts in sorted(series, key=lambda ns: smapes[ns[0]]):
+    smape = smapes[name]
+    if smape <= max(50, sorted(smapes.values())[:2][-1]):
+        ax.plot(ts.to_pd(), label=f"{name} (sMAPE={smape:.1f})")
+ax.legend()
+plt.show()
+
+
+
+
+
+
+
+../../_images/tutorials_advanced_2_ForecastInvertPOC_8_0.png +
+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
+ +
+
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+ + + +
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+ +
+
+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/advanced/2_ForecastInvertPOC.ipynb b/v2.0.2/tutorials/advanced/2_ForecastInvertPOC.ipynb new file mode 100644 index 000000000..0ca736b8c --- /dev/null +++ b/v2.0.2/tutorials/advanced/2_ForecastInvertPOC.ipynb @@ -0,0 +1,538 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Proof of Concept: Inverse Transforms for Forecasters" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 414/414 [00:00<00:00, 861.84it/s]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "from merlion.utils import TimeSeries\n", + "from ts_datasets.forecast import M4\n", + "\n", + "ts, md = M4(subset=\"Hourly\")[2]\n", + "train = TimeSeries.from_pd(ts[md[\"trainval\"]])\n", + "test = TimeSeries.from_pd(ts[~md[\"trainval\"]])\n", + "\n", + "ax = plt.figure(figsize=(10, 6)).add_subplot(111)\n", + "ax.plot(ts)\n", + "ax.axvline(train.to_pd().index[-1], ls=\"--\", c=\"k\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "from merlion.evaluate.forecast import ForecastMetric\n", + "from merlion.models.forecast.base import ForecasterBase\n", + "from merlion.models.forecast.prophet import Prophet, ProphetConfig\n", + "from merlion.transform.resample import TemporalResample\n", + "from merlion.transform.sequence import TransformSequence\n", + "from merlion.utils import TimeSeries\n", + "\n", + "def get_model(transform=None):\n", + " if transform is not None:\n", + " transform = TransformSequence([TemporalResample(), transform])\n", + " prophet = Prophet(ProphetConfig(add_seasonality=\"auto\", transform=transform))\n", + " return prophet\n", + "\n", + "def eval_model(model: ForecasterBase, train_data: TimeSeries, test_data: TimeSeries,\n", + " apply_inverse=True):\n", + " og_train = train_data\n", + " model.config.invert_transform = apply_inverse\n", + " yhat_train, _ = model.train(train_data)\n", + " if not apply_inverse:\n", + " train_data = model.transform(train_data)\n", + " \n", + " t = test_data.time_stamps\n", + " yhat_test, _ = model.forecast(t)\n", + " if not apply_inverse:\n", + " test_data = model.transform(og_train + test_data).align(reference=t)\n", + " \n", + " print(f\"Train sMAPE: {ForecastMetric.sMAPE.value(train_data, yhat_train):.2f}\")\n", + " print(f\"Test sMAPE: {ForecastMetric.sMAPE.value(test_data, yhat_test):.2f}\")\n", + "\n", + " ax = plt.figure(figsize=(10, 6)).add_subplot(111)\n", + " ax.plot((train_data + test_data).to_pd(), label=\"true\")\n", + " ax.plot((yhat_train + yhat_test).to_pd(), label=\"model\")\n", + " ax.axvline(pd.to_datetime(t[0], unit=\"s\"), c=\"k\", ls=\"--\")\n", + " ax.legend()\n", + " plt.show()\n", + " return yhat_test" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21:14:26 - cmdstanpy - INFO - Chain [1] start processing\n", + "21:14:26 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No transform...\n", + "Train sMAPE: 4.88\n", + "Test sMAPE: 17.58\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(\"No transform...\")\n", + "base = eval_model(get_model(), train, test, apply_inverse=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalize...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21:14:26 - cmdstanpy - INFO - Chain [1] start processing\n", + "21:14:26 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train sMAPE: 54.41\n", + "Test sMAPE: 118.27\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalize + invert...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21:14:27 - cmdstanpy - INFO - Chain [1] start processing\n", + "21:14:27 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train sMAPE: 5.73\n", + "Test sMAPE: 17.55\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from merlion.transform.normalize import MeanVarNormalize, MinMaxNormalize\n", + "\n", + "print(\"Normalize...\")\n", + "eval_model(get_model(MeanVarNormalize()), train, test, apply_inverse=False)\n", + "\n", + "print(\"Normalize + invert...\")\n", + "norm = eval_model(get_model(MeanVarNormalize()), train, test, apply_inverse=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Box-Cox transform...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21:14:27 - cmdstanpy - INFO - Chain [1] start processing\n", + "21:14:27 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train sMAPE: 0.99\n", + "Test sMAPE: 3.36\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Box-Cox transform + invert...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21:14:28 - cmdstanpy - INFO - Chain [1] start processing\n", + "21:14:28 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train sMAPE: 3.61\n", + "Test sMAPE: 12.30\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from merlion.transform.normalize import BoxCoxTransform\n", + "\n", + "print(\"Box-Cox transform...\")\n", + "eval_model(get_model(BoxCoxTransform()), train, test, apply_inverse=False)\n", + "\n", + "print(\"Box-Cox transform + invert...\")\n", + "boxcox = eval_model(get_model(BoxCoxTransform()), train, test, apply_inverse=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Moving Average...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21:14:29 - cmdstanpy - INFO - Chain [1] start processing\n", + "21:14:29 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train sMAPE: 4.46\n", + "Test sMAPE: 17.09\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Moving Average + invert...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21:14:29 - cmdstanpy - INFO - Chain [1] start processing\n", + "21:14:29 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train sMAPE: 5.49\n", + "Test sMAPE: 17.88\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from merlion.transform.moving_average import MovingAverage\n", + "\n", + "print(\"Moving Average...\")\n", + "eval_model(get_model(MovingAverage(n_steps=5)), train, test, apply_inverse=False)\n", + "\n", + "print(\"Moving Average + invert...\")\n", + "ma = eval_model(get_model(MovingAverage(n_steps=5)), train, test, apply_inverse=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Difference transform..." + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21:14:30 - cmdstanpy - INFO - Chain [1] start processing\n", + "21:14:30 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Train sMAPE: 53.17\n", + "Test sMAPE: 48.12\n" + ] + }, + { + "data": { + "image/png": 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9MfQCfPamw2f876rgDQP2gIl+09LrtISqyq47Hn7iQVsAAF+/o1lUz+XwAoZnvf2reOn7v7/Rp3LWRAPASkYcPHlhPfAUT3/V1YFFDNhpAGAbwIC96dO34N+9//v43j1LZ/xvA0kG7FzUwD2Q4qTsLNHE+kLNKXVTkLvmWwBOjy61ibM31Hv0jTtPbPCZiDgbGPIGgJWM08GAhbEXou7uPVhnJWVCy7YBLZFODcSied/y6Iz/bWDq+psU5Fkdp0MH2UQ0L63X33BSU1bRxAMj1HsUbpJ5dbOcR140NhQlI/4wa09Usc+Y1K2klJ+xXgA3/fWZivmWBQBYGfln/G8D0wxYk4I8m+NEw4Cdlggl81V3Q7ZeANfEAyPUGlm0rDztaU+7386BTa1vpnG//anTEg0DVjJOCwDjgG2KW143/RXodkY1AVjs767H90d1Bagac+sEYF7A8ODXfhof+PY9tY6PM2DfuPMkLn71NfjkD++rdR7nOoDjnK+L9TiyMsbFr74GX7+jXsri5EAwYI7ZTGPrCTWn1N2QqUWvrj1PEw+MKFvZ/7rXvQ6ve93r7pdziK/NZ8OGoJm5SkYCgK2jF6SjAdj6Kin7NVOQcd1XXc3Hd/efwqNe/3kcWx1XPnahZQMAVmsCsKEXIGQc//mfb6p1fNocccex6tVbT3zLl/DL7/hGrXN4oMT/+NxteOgff7Y2GP/8zUcAAJ/YVx0AA8ApyYD13IbIX08oBqu2wbQcU3VZ/SYeGLERGZXpiL+DGyGxqRoNACsZ8ZdrPT5gjuRE66Lz08mAffnWY7WEivecHMILGe5bqQ7AXEu8cnUZsPXm9dOut6odx90nBjjR93DjoZXa5zHyQnzzzrO7auyvvnIngPoLr2oqf8l53VrHqzF0FmhtN3WE62SwNgMDxjnHV28/flYIrx+owUre++c85zl4znOec7+cQ5yR3wyAsCgaAFYyTgcDJgDY+lKQTAOwemaucebtH75zL6697XjlzxhJxqNOJaa6j8vDmgAsNsjrpL/SblnVZzGdMvvUDffhSEUw+uqP3YDfeve3cHhlY4oRTkeoe1kXFN9yeA0A4Fj1hBrrBQ5NCOASrjcFqUX4G/cc/uE7B/DCv/0OPlFDTtDE6Ymy88BoNMJodP/Me/G54GyYF04LACOE/C0h5Bgh5KbY97YQQr5ACPmx/P+i/D4hhPwFIeQOQsgNhJDHno5zuL8jPA0MWMg4HMkA1f2M+CQ5rJH6mZ5kV8fVgdBY+gYNvDoASPz908GAHVqqPojTdmlVFx41sCkRIPAPP/gDPP9vvl3pM75z9ykAUVXo2Rx1DX0VaF1vOr6uMXITp6c5/WYAwrcfFWC+KczYuNgMVYfxd/BcYsDeA+DZU997NYAvcc4vAfAl+W8AeA6AS+R/LwbwztN0DvdrBKeFAYNOQda1QIi/5IMaDNQ021NHP6MYsDp/X53+6QBgyzU+I+2uVwUA8WqfsS+OrWqroe7dA8HLqjabu86y9fjCv1Gpp8EkwIv//voNs1VRcd/yCP/P31+Pe08OKx0XB8/r7XG7kaJnpUNs1WRTm1h/bAoAFnsHz4bm8qcFgHHOvwrg1NS3fxHAe+XX7wXwS7Hv/z0X8S0AC4SQnafjPO7PiDMn3joWHJWCrCsQDBjTk0wdADRtvmoZ1V8BDcBq6NDUIB3VFG7HB/lKjTRmWtq2KhiOg3FVTEAJqfQZ6vofCF5WdSdedVTdnWo8Hb1RjdWvufEwPn/zUbz187dtyN9X8Rvv+ia+cPNRfPvuarrC08XsAxvLgCk3/7bdALCNivA0bIL+8l/vwEved33t48+2FOT9WT50Hudc9Zw5AuA8+fUFAA7Efu+g/N7G9acpEWqXb1KyrolKidDrpm1CxjHXMjHyw1pmrNNsRZ3Fb+QpDViNFKRKG50GALY8qs4eqTnCMohetMOKi3cYe3YnZQqxCv7inOu/fbYyYCNv/WJX9SzWm4IEBHiwN8COQjEvG73wHzglGLiqOqz4PayvARP/38gFT21oNuIdaEJEWU3yz/3cz2X+7H98TmxkDpwaYs+WNv7gg9/Hp288jLvf/NxSn32upiBzg4v8QOW7QQh5MSHkekLI9cePVxeLn85QE1XLMtZVLaRTkDV37CHjWGxLK4ca+q1p4FcnfaQWnTpmsOE6BbvxBWNpUIMBk3/fjjF/VVsyxfHCiTXBYFVhwOK6rxODs5MBizN39f3Q1if+Ph2FMesNxbxsZOornn6tyoonQOw6U5Ab6YSvNgRnw6L7QI2y9/5Vr3oVXvWqV6X+7EHbOgCAz94kLGquueFwpSrnhA1FKKQJNxxcLv8BZzjuTwB2VKUW5f+Pye8fArAn9nu75fdmgnP+Ls75lZzzK7dv334/nmpxKODQso3aE43QgK1fhL+lIwBYHR3VDANW4zx0FWSNFKQao7UBWGw0rkcDRmkEmKqC0DgDpoCIQcsDsLhO4cTa2cmAxa9hvQCqbgozKQvYGAA22gQALF4MU3VTlDSYXr8Wb6NCzUnnujnyRsbp0IBt6zoA6slbgOQ76IcM7/vWPfiF//N1XPfjzdGfcjruTwD2CQC/I7/+HQD/Evv+C2U15OMBrMRSlZs21KLbc81E+qXSZ3CuS+7rCpdDxrG4HgA2xfbUKQYYracKMqYBqyOcjt+3lWF18KIW7ThdXnXiiAMOBcAq4K/E3zt5ljJg6hq2YBV8sFTrMxQLXBfAxd+FjVr8FRvsbmAKcinGqNYFYA837sNzwy/W+vsagG0g+FFMZN159QET9+0D3vkkYHDmPQbL+oA99alPxVOf+tTUn6n1qe5zTDJgXFvd3HNqUOvz7u84XTYU/wDgmwAeSgg5SAj5PQBvAfAMQsiPATxd/hsAPg3gLgB3AHg3gJedjnO4v0PNLV3Xqi0gZzEn/PUYsW5dBwCbTn2uiwGrmYK04eML5h8huOWaysez9TJgqvIuIeCur5tRZe9VUpAJHVtNP7TTFkv3AGH9atrvu/8OD/nn6QLocqHue20NWMITboMYMDkWCKoVYZzOWBn5eAa9Ho8gd6E/rvYsFfj9jPUq/Cl/J4Ia7L56DH7Ia3kTro59vPqfbsBaDUmFCgWEq8oJNl188y+Bf/nD2oeffN+LgKM3Aff94PSdU8k4Henf9bbFSjDzIdPa3M3qz3u6qiB/i3O+k3Nucc53c87/hnN+knP+NM75JZzzp3POT8nf5ZzzP+CcP5hz/kjOef2ShzMYCpnPuSb8kNfacZ8OI9aQcXQdEyYltdr5qL+7o+fUPo8LBzfhWvuPwIbLlY9ljOPJ9EY8iB4BvfYtxQdMxXrBizo8Pk9XHezx3z/er64Bi19DXTb1dMTkyG3A/3oUDl7z3yofGzCGS8hBAIA9qGd+ebp6EF5O9sPzNoZJVMzLRpa8L/dHeLf9P/Ep548rp25CxjGHiB34m2tvrvz3w3Wmgt/3zXvwj989gG995H8CB75b+XggkkOc9QzY514L/OB9tQ5ljGPr6G75j3opvPWEGo+vN98DfPiFtT5Ds+JT71FZYD+dgqQagG3O96IpGSkZatF8HLsBzze+UGvhDBmHZVBQUq8KUrlWmwbFXMtaVwryv/7i5QDq7Rh/f+UvcDE9ih3D6qX3jHM8m4pJ9sP3bcPRiv0k1XOgBFheRwry6fg2bnNeiB6GlQFAIoW4jhSkbdDaWofTESduEimnO26sZiILCND0k/QG8bVVvZVQ3IG9rnYkYBxXkVtxjfNazN/4d7U+482fvgXPettXa0/QSvS+oW14Dv9Qf121R2zIOC4lUVH6HfdWB9NJHVn1+9C2Dcyhj2fc+SbgPeWq3aZjtE4g/OZP34In/7cv1zr2tMVoOfq6xvs4msTmw8nq+s+nYoj3gONF5ueBm/+l8PfTQgEv00uef9n3Kv78vZBrZnpzwq8GgCXiwKlhJtJWk8wrDr0Kb7T+DkO/ngCdEgLToLUmKnVqJiWYrwvA5A5jPVq0veF+AIDvV//7IQPOJ8IyrkUm+OGB5YrHi/Odb1m1NGhqXnur+Q44JMBucrxyCixgDLZBsQWrOLYs2ANaAYEpxmA9esLTEe1j+wAAJ+jWyseGjGOOCNNPZriVj4+nwtfTAudZhgDzZFRd83JoeYS//upd2HL8WwgO/bD4gJRQY3C9DNgkCHGopplr+7AA0MfINvQrpvECxtEmEXvIRtX7m8ZlARO/+n2Ycy08hcomKk6v8vGAMET+BfoNPOGWP6t1/F9/9S4cXBoh3EgR/33fj772qmuWRoO16B/j+n1q60bIOS4n96zrMwLG8ULjc3j1Dc8S8ggZZceXmssfS24HGS+fGynIB0LcebyPp/z3f8Vf/usdqT8PGccCohe8jv6JcY7zJnfjw8afwJosVz5esVcmCdfNgKmqrcoLB4sAw/QupdThPJrwexihVVG8rABYtyZ4YZyDgOlzaGNcowqS48GtNXzf/Xd49sqHANRLQV5mH8Eub3+lv306w1m6HQDQ5tXc0wEB3FuQFaB+9cVCvXcUDJ1RvRqckHFcQUVDcB925eNP9T1QMPyD/Wew/u9P1TqHlZGPx5Lb8dT9b1vXLP8HH/gBnvSWL9fSUBl9wVoxw6nsDcg4RxsRAOM1mBPGOSwE+D3j0/Am1d+Ftm3gSirZ9G2XVj5encNf2P8Hl9/30VrHA8AT6I9gvGEROHZr7c9YV4xixSw1ANR4GANgdRkwzoH3/ypw22cqHxoyjodIWQKAxFoRj1//9V/Hr//6r6f+zA8ZXmV+RPyjf1R/vyzD7Icc5+MkPua8Hudd9yd6Xi5bIHCmowFgMlRfwW/fPW3oLyKITfYAMB5VX7RCxvHc/W/GFeR2HPvRV7H/RLXJKmRionvZV38CL5r8A1YrCm6BiG1wJQCrnP6J7cwsfy3nF9MjZBwdiLRjl4wqO8gr9uj/Dd+HP5i8u/Lf5xyYj2leemRYOR0cMo4nYx8A4DFUAHaDEnzuR0dwcKn4vVD3/H2jP8QnjVfVWnRPR1AJnDqovmgGjKEtn6MRjoCwIvMiQe+fW+/Ef7791wC/OvsTMo5FuSkik+rvos8YHktur3xcPPqTAB9zXo8nHPsQEFRLp8fji7eIxaZWcY4ckx0+qFwFGYRcP0cAIDUW7pABLzE+iddZ74dz04erH885ekQ+f69f+XgA2IYYeKn4LgKCUf8jU4K3ozfl//L9FfExUAOATYaxZzeuCcCO3ADc8UXgEy+vfGjIODoxNhUZY/JlL3sZXvay9No7FoaaWY+DyLK+mX7I8HRDMImD5WO65+5m1QY2AEyGejxZeIAxji6iATLpVy+9Z5xjd/9GAMCpwQTPeNtXKh0fMo4n0B8BAJ4y/EItEX6oAZhy5K/4YvoRwLCD6ose4xFzMoehrl4qGwETDNbzJh/Dv+GfrvX34zv+BTqqJcJ/PN8HADjIt+vPfcn7vodffsc3Co8PGcdF5Ij+93iDDCxJIN7nDqvBgE2lrrIm26xQ2sNfNr4uvjGqPp7EhC/AA5lUX7D8gOEnqGA7wt7uyscDAEksmuvX3dSp5jRDcQ4tNqicggynniP1aoxpxvFThkjh+rAqHx8yjpYCgTUAIOccl+De6Bs1nsPOeRdXKxZuo9gSLzYOa9wHb3QaGLAff178f9cVlQ8Vc3tsE5JxDsPhEMNh+pxjhrHjx3EAVjIFyTieLNPZR/ki7jwuAP1GmgTnRQPAZCgRLslAYGLBiV4Or04FYGxg9zCs7IYfMo6nUYHuj3Yfrl3Yq34GEGsKXnHHzWMMmBtW360mUpBkWHnBYYzjEWR/4t9VgnOhPVOx1RjXeg47uNAcKVCuyv+Pl3gmIeN4ogTSQFRJd6aDKgDGqz/HMEwC2ao79iBMVt/VWTRDDn0ORg3g4Iccc0ScA68p070gjHVVq7no/cWXfqy/riPmV4uWyX0E3qhSQUEY2xABgOmvVS5ICDnHZVL7wyfV36Ug/i7VYH4ClmTxUAOM75iLdIz91TPvoQUAiKfya9wHbxi797UZMEEQwKie0g9Kzgk/+7M/i5/92Z9N/ZnN0kFo2XXCD5nOcMTXl3ENbeKZiAaAyVBzTlZCLGTJlyscruB57/wG3nntnRlHzIbJoioVTbNWiIBx7JQC9o4RYm0SVE85SMBiGURUY1YEH8E4miS6fFB5ZyF2u0oDVo8BeziNxJlV2aNpBuzyrUln+7LnoFk8+RyVHq9MRlWU/ksBOyf1hficr0tsSyWb2eXrS0ECqAw+/JBhB4mxXrVSX5GWrxYAY0y/C7RGChMArHD9DNjbvnArfoleBwderZ26GTuHNutXSmOGjGkWEQA6fFjZ55AxDoOIv8nHy5WOBeS7pDZF49XKDJQfsqmFv/pzMHh0zSdPrsM1/dP/Ebj98/WOjTNgdYDoOAbAar7PeoNdUwuYYMVrPAeLrZMBCzla8n3uxTJWVdeZMxUNACsZMwBstILr71nCf/tsOcEmm9ql9VBPQ6YW/q48/nDFyqmQcfwb40tY+MabYRq0sg0Fi4ls58igckPuMIxpwDDCuCL4YDENGYBMKjvz+BhrAgBdPqwuwg8jql09RwVs4z0mM4/nEZtKCcdgXFM79OU3Am+5sB4IY0xotyC0Q1UjYBwtMkHIJeKsONnOLpo1riH0YEK8P0YNPaIfMP0ciLeWNIcrGVa4PuYFAJ5nfBVvt9+B5xtfqMWAxRetOTKsNCZVMQWjNjgIemSE1VG1TR1jTD+HOotufEMDHiZkDmXCD6fT4dXPIQ5i6+jgAACn7gK+8y7gI79T73h/fQAslOzjKunVZmNvO3Ck/t9nSLCpdc4hsaGJHV9ahM8YOkQQHbvciPDYKJlHUTQATIZKQWQxGKFccPS/h9U0KyFPChR7NRkwNdG0mFg0D69U9NEKGd5k/Q263/0LWJRUBx+xFGQPo8o+ViabgBKOU7wLg3AE42oLZ2KyBjDpL1c6nvNohwQAbT6oDEIDxuHyKI0aD9ssAcCmwPxkUJPF+tpbxf8HNXbsQZz1qA7ARDHFBEexKL5RcbKd0ZDVmPDtGPipUxDix1ImBByowaLZbP0M2NOocC1noLU0YFY4gg8TgCgwGVbY7YeSEQ6tLgKzgx6GlR3pKfdhQpx3nWKIcGpTVfU+BiFLLvw13iUjiMZxXTZUM1/bH1bveH8IOHPi6xpMYig3x6follrvIucc/kiyaLUkAdMpyOqf4fD4exA9xyoMmDqHBRpnwJoU5KaOohRkMLVojisu/GxKa1GHAWOxc3Ck/urwSjUGrNO/W39tGrSyB5ZiwAakIxiwigyWEYrrPsq3iG9UHKTTNLdXsQ/hNAPW4QOENZpxu3LBWKRJAOzUAGD+YLnS3xcHra9iSu22j/EFOPCAoJqeMJAs4DG+IL5RVYQ/vWiug7VgnMCqURASsPWlrhjjyQWjJuvwKGM/AMBGUI8B42McJ2I89cgIH//BodIpl1BqW5nZQmh30cMQqxUBmM3WxwKKtNEEQy66c1R9n/0Z7dH6GDBaw14HAHB4n/h/e0u9470h0FoATLfWmFZz8zLdUutdvPN4f11aPCUJWOLSmLniOXCeHE88dg5lx0UQRqy2y6KNZZOC3OShAVgGBRbKCg9mtQEAXlUAxpBIQc6R6mX3cfbH8FZBSHUGbHHlFv21Q1nlZtxsIhbuZXMb5lG97F1NdGrhruo7NC309CqCF86jyjm0tqDF+pXvQcg4XDlRXND28e9/5iH6Z6VSkFNsai0A1j8WfV1n4ZdM5lEFoKqyDnKyPc4VA1aRyVyniB+IUm9HsAg7qC7+9gKWeA6VdWxx7RJQmwGzIVIlPTKsZUNhszFOEfEcehjif3zuNrzp07cUHCVCbSyZ1QG3umiTceUUZByA1UnfKSB8hNdjU/2QrTsFacWYzDp6QgCRhUZdXaY/AKw2YHeTerCSofRba+ZC5TQuANx8eC0aD5PqWjyVgjyqnmMGi/eiF70IL3rRi2a+709Zohw7flx/XXZceDF/QieMA7CGATsrIlOELwc5b+8A4wS+rIJUdg5FEU4xN/U0YCxWdr+GedfEyX61djw0NjB7dFKZAVODfGhtQZtMKmvALMmA/dRjHg4AYBUnmumqrXd//geVhMuMx3QKc7vQYsPK94CwCQyVchmvYs4x9c/qpCDDGu7jSb1IjYVfHn+Kz81+XolQm4GTXDqXV/TxEgCuftoJACymWLxFmGwCBNXGgmJOJtyqdQ7TC0ZdBszlEoBhWMtJ3mZjrNB58VlS/1J2Y6ZYdW61YTpttODj+/dWY5UdHgcv9TVgx7EgvlF5MyCewypv1zoeSDJgdfSEACLQVLcC0R8JAGa1awEotalao/OAX11XOpgE0bzEgspjWr1LK+ggpHbmpiwLgAUsYsWP8QXcfSgyaC7LgIWBDxceQE1YbAwTYjPR2FBs8ijyAdMpSLuDIW1jbVnaEDjlfG/iKchl3qldBRmJVRm2Wn5lajWudVgg1T2wFACb2FvgwqusAdOi5Y7wz6q602NT2qH+6hK+cWf5snEe1yl0d8Blg8r3wJT2DeieDzAfLRot/GUAmKjEHIMRAdzqtH9Zr2eQmuBPQgGwisUcIUMHY5yqebwX8NMg2BXv0knMy3OopmVTE75mAasyL0HEvAzRqrfwxryTemQEL6y4UHAOm42wSiQAk2yaMlouClVMwcw2LLeDnW2Oj1x/sJIVhUobjbkFswZ4YYEPhwQ4oTYDQUUwLzfHa2hhTNx1ib+P87la1wAgAk11RfzeELA7gNWqBcCIN8SYW+Jd9IeVGSwBwMZY5h3xjarWMkzoa0fcgU/dTBB44sQJnDgxq1uN60KP8MUESVHWKogqbWtvp/if/IwmBbnJI/LoSkdgGkDZbVC7oye6nmum/v7M8THW42js5aqi+QgCQdVPHNG7b4s5xriiZsSI7fQWaPUKQC4X/sBdRAsT3Yy4bJiK6m+La+BVJ1t5H31bLDhzZFBKd6WCSR8wDgK0tsBik8r3QFfqzIlB3om18ikDwNSi57d3AAB4LQZrfZ5BCsCd0gxWtQmf+xMYhGONtxESqwaDFumvTpKtta7BkamvFVIXBIqFWxcSVNYeCQA35hbWSKfewhuI+wgAcxhUZ8BCHwYYVg0xHhSodUuOCS2At9uA1cKiHeDI6rjSpkQ9h+PYAsuv4WQvn1vExlZ8jiFDC2LhH9FOzXdJSSMW610DoBkov6IuVYdKQVqtWp0hJqM1DOFgBBsAr6zrHI8nsEmIYzVTwSEXhTlDOPCIkzknPO95z8Pznve8me/HZQnH+UKiMKPsxoQogkEBMCn1aVKQmzwiDVj6zzU6t8VOUeXKO065nWZc93OML6ArX4xhBQaJB2NQwuG1xMK9xRjVYMCigT1HR5Xb8MAfIuQE3F1AC15lAGaHSQAGryLNLRmDoHM+ALHDqeKjxSE1L2ZLPEs2qXwPdLqiI55DPA1VVgPWwQShvIZaZe/e6U5BVpzwA5mKhoOAOpXb8CjhNQPBcczrlH6VUKaNK0QxYNWuQaUQT3B1fDUQ6UkrjQFc9NFeVzEEoBiwquNRpZ0WAEQFJk5ZaYRk1bnVAcwWLCaOr7IxVAzYSbpFFwdVCSrfpVOotxlQC/cQDiak+rsIRDq243wBZg09IQBwed4WG9dqhwRvKMCX1a78Ln/jzhNYWl7CEC7GXJqoVtzcerIi/VjNVDCTa5xHXIyJU12WECvMWUJPp9MBwA/KbQh0hqer5mbxeQ0DdpZElgZM+XgRqwPTbaMlGbCuU5IBi1XfncScZtCqiNiZ3GEpADZveNUBWBhNbnNkVNkFnntDDOHCcDughGNSsSemFruqFGTldIMAwlwe3yOjavdQPgdmCq2Fxart9oGY71JnGwCgTaqlIFXrFXUNtEYz6wQDtgEpSCKPH0KlG6p6N4kU5hgOVkIb199+qNLxAOBIK5A1ox6AUhN+beZFMmgjOBih+oIDACzmHD9XRwMmgfiEtsENBy35LqpOF0WhWhFxqwVYLf1uVwFgrn4OC7D5pLKfGg3WtxlQBSEjBcBqPAc1L51EDwar3mEEmOoCUMfKwh9hRFz0eXVG+djqBC4mGHMbQwXAqsoKpMn2yZqp4FCukdxuCxBYEQj78l30uIE13kraDZXcmJgKgMlKVPUZjQ/Ypo/8RVilvojdAbHaGkCVnejiDuyn+JwGcJVsHCQAC1zxcnUNv/KEHRebzstWDR/89r3lhej+AGPYsByhE/DG1cCD2mGrAUKqCj1lKpg6PYy5BRd+RQCmNC9iwTHZGJxXa0puTbF425zo3tkl3ge96LVkuXrFiQ7A+hmwdaYgidR3TGBLAFYvlTwmLkbcSVYjlgzlwbVK6zFgoS9SLidrMi++rLiayGuo1VBcWgcExBStUyozYFISQB3AasGVc4xplGtyHzIOBz6IHg+SAatwHooBG1gydVVx4TV0CrLec1B6wiF3MEF26isvVBr1JJ8XHUtYjQXbG2KVt8TXtdjQAf7pxlO4bv+wBpvLYCMAsVyMagIw1eXkVM3CmjC2Rg65XWtD1MZYb2jUGgsIvWWZ0BKbttgcKxatSUFu8ihKQapFk9htwHTx4EWx0LKSQsdQChQ5CFZ4Bw7xQcEqgQcuLSACuXD3qF8Z2ZvhSFcLdTHEV28/jtf+8434h+/cW3CkCOIPhV+PJSaaqgDMUO2Y3AUAAA0rpq7kIDfdjhykE92HsUwoET6T1UYmF8+hSk9Me4oB29aKAbASKchIT9jFEI4GM5VCGeJ2z6tl2qgZsJpVkDwUC3VIbfi0OvjwQ2EBMSEuRrATk23Z0Au/uSA/tKqOTaYweRcMtHLlmGLxJtQVC04NIK28m/r2dsxhWN0HTF6DZ4jUldrYlU3ZhIzDhg9Ytt6QAFUZMCnCVwCs4rugepIuowsOWo8BwwRj0sK4JhNpy2uoCz4AMTdq/VRNXWaf2ULDVfFdVkA6pDaGkH5qVTe30kbjZE0mkrEALvFBrLYEYHUkAROMIECkQwIYssNC2Q2BTkHKubm1yVOQ5fJn50Co6Ypm+YCpCkS7A1ht7O6s4cqLFkszJ4oBC4wWfubhFwO3iIol1UOwTBCZdmKuYF66hofxqDoAO45FzGGYqDJZLQliiD/CEA649EMLKzbf1QCsJSYqWofmJmKXtTA/j9YpD6eqMGDSj42ZAkgD4jlUYsBYcpe1xYw/w+LPCaR2aGy34cEGDWswYCoF2Tt/XSnIZXQT/y4bRAp8WW0AxtHDBB51MYKTrIgsGbZMfQ2MBfmh1QAUjaVRQ8NJWLSUCU+CSJ+2MAwswK9hXqlsXZztOH98BBO/mqZS3ffQcEBISzOJZUXLgVy4x6YLWIAhN0RVHPmVKbHnSEbXHwLYWvp49f6PuI3AcGFV1g7FtEdg9RgwPoZPbAwgm3IHY8DpVjgJD4QHOMoX8RDcVz0FyTkQTjCBjRG3wf1RphwmLXzG4RAJwLhc1iveR7XBr1vZrET/3GwJFi7jObz0pS9NP1ylkolM6UPMzQO0SjNgusq+rQCYWG/q2LuciWgAmAzFZOUxYC48KZIUVSrUIuUBmFz4Q7OFR+/dCdwi0PnysMLOXy66YUtMbh3iV26ca4YjsegaNjoxAJYFPGcinGACC7YCYONqk53BFQMmBrkRjsE5zzTAnfnzEggTpwtitdAzPPQreJFxAA7xwc2uZvFaqFYJ6Wgd2zZ9DZATRik9WejBJAzE7mBCnERhROnwhgCISIPW2W3LyVK7VlfVgEkGLCC2rHiqJ7j1aQtjbiesPMqGxT0ExBRgGqiRRpXghzsIaPWF3w8YOphgQucx5Da4P6y0aAIAlwyy52wFJRxhxaIU9RxD6gBGxICVZbBY6It30XQAy4DBPFCwagyYBM/MWRDfqMqAyXfJg1ULgKm+oj5tYYSwFhB2+Bi+4cInEoBVBXH+VCFB1UKAUAIFbkoBe0UGLGSSAethGCgGrCIj7CkNWL1UcATAZCWmn74x/I3f+I30w0MOFxNM4GIMkUZtSQBWlgHTVfYdsUb+0iO2YOLvwBdvOYaQcRi06gi9f6NJQU4FyZhCQzlRwXR1lYpBSPkUpDRiDQxptAegRTycGlSolpG6HS4BWIt6lXPbVjgSXjmxdAUAlMicARDMhwdLXwPzqmrA5N80XXjEwVp/DXtf82ncdKjcpBnKyZbIsvku9XHf8qi0ho1xsePnhp14DlX6QVpc6djkLj+2YJQC5HJiI3ZbALCKaVj9GZKNrZMuQTABAxXVe7FzKhtEmp6GVAKwiiBSCW5FCtLRi3iVMLmPkFhCzwfUBpFjXUhQncVrYYJQ7/hrpCBVYY1kj1hVACYXbkathAasNICSiyYxXc0IO/AqacBcPoZPXVCnLpsqwQes2s/BhYfQcDCBVes5uHyMgLrwaL30nZqb66b0FWDzYIkUYg1NpY0AzLAxYPU0YKqwpm4xBAnV3G7naiIPHDiAAwcOzHzflyDSp/J4RBqu0gBMzaVSpvOMS3q48mLx9WY0Y20AmIwiHKUmCRi2NsozKEHZzJVa+JkR6ac6xMPSoAoDJgYIkwt/G9WrIC0mKs9gtROLnkHLvQokFM7hxBbXEE6qM2A+LIAQ+NTVIPAD376n3N+XVhxEgo828fDZHx3B6/7lplLHcy4mKv0cIXbwVVKQ5lQaFf5Qe5GVAeS68MBqwSMujLBG1ZWnPINqumaHEwTEgg8DQQ3dDYlpwNbDgI2J2C23amjALO4hoI6o4ANqsBaqkMCqXckp2FQBItfTPsZzonepUuhUsJhXWhUXLK4AmOVEGxJ45Rk0xtGCJwCYKwpzeFUAJseTBwt+DfYnYAw2fMG88HoifIv7CAxHpNOBGu+S+P0lyR5V1cYiBkJH3AFhQSUrCz8UWj5GbQyYTGxVZdZjWjzxoVXnBDGeOHVyRfgveMEL8IIXvGD2z6s0KrE1A6bWqHHJYjU6MzeP9Ny8GYX4DQCToZfNDIYyQveOTkESUr56jsV2KGqi295iOFUlBamM9eTL1SJe5dy2zRQD1tJl/ABQsmgKJPTgwQKx6jEnFvfhE9E9IDBcrf354YFyDBhRAnhZtaX0I1+4+Wip4xkXO3xuJhecKiJ8k88O8m+95mm4YKFV6n1Q4MWwXPjU0cLnSuEPwaw2Dg1QkwHzEBALAMG4RgUfYREAm5Ca4AU+PIjdrkXCyt5J1joZMOg0qlVLx+aFDA48EEumTIJxZfdxZUPhSwaMVwVxkjnhhgPYHTz6PBvnzTmlAZR6F6nlJlLypVu/cCX+dmDask/uqBr4UIsmN2zBQFUF874Pg3Bww8EQ9ZhIC2LhD6hKQVYckyp9J/VTk2FFL7FYGlaBD1TILoRMjCcYTm0GjMn1ZcBdcGpWZzLlGslNG6MaTGQgQWRAZSECxNy8d1sHx9bKbVIjjfGC+L8/0l0hNqMQvwFgMlTrjUwcolgKtXAHIxikQhUk53DgSQZMDPLtDqvEgOnJ0m4D1ISLCbyQVWJv7HAkFkyrDSeRgiyHwAQAM8U5AOAVB5nJPAREDK6QOppiPrBUcrAHEc0Nq61B5EN2lBPMcg7JgDmoqwGzuAQKjvLLGWOxY2PPllY5C6RQsQ4ufOpGvmJVwhvi8MjA525fRViDeUEw1kC4TtUV1QyYA4/YNSoIxcI9iS84Fc/B5EJ0rFJndVk8Rm14dRkwBDAsByNug/DqIFKxZp4dF7BXCJWClIyuxcY4f75VWkSvGDBqRuPBJV5pEX/IOGzig1ELpivmhMmoYmFODATWsZFg6t0zbAyZfJcrAGHOuQDzNA7A6jFgKgVZ9R6ozbXHTS1Ar/I++6F4Dtyw0Wf1xhP8CAQys7obvyYpDAdD5lR+DgET4ykglr4Hv/7orbhgoYX7SvY21QDM6gDUkgBMwJwqhSVnKhoANhVZYnC14EABKM7gkKCSCN8mATiNGLBtbohTVVKQOl3QSqQQq+S2LSbK5qcZMFoWgDHBgEECsKo+Xib39MLPzJa2H7BKitD0czBdqXkRx+9aaJU6nvFoolLPwSVeJTNWIf62AcMUqUw50RmUICwx4VAJIqnlIqROPQDmD3TJetVnAAAIPXgS+Iy4XbkpOtELv1Wr9D+QC4YHq9aCAwA2PATUhmVZ4p2sq2MzZOuUqsaRoUh9GZYr7gFQwwpDefstiHOqWXmmU+reEI5BSzO66h6IFKRKyZdPQXIObX9gSG9Av2L6jcp3yXXraekUACOmZMAqtuFRFg7MsBHUBPORr54AYFVZQAWkPVgxH6/y71J0DQ76KgVZYVPEOdes9gQWmFF9Q6Iqo7lhY6Aa3FcYU4oB84mt3fyf/7jt2Dnv4vByueeh5SGGpfWxrtkwYJs+tA9Yxs+TKUi5cFewL0jTgG21QixVSUHKczBkukAxWKVz25zD4WPJgLlTKchyAIxKDZhiwKpaKJjc1wwYrMjtuKyInoTJBWdXh8vjqz0HBeAAQXNXGZwm9xFQS1+DmqwpKVkVG0asQ2C4sHiFd0CFNwQzWxhxB5RX04sAAIIJfFkEPYRbOfWlFk1O7VqshdDtBJhws9aCAwgmkxEbtkkFAKpceRa9S3Uc1H2fwSU+TKelUybVF+6RsB2wBIM7rsqcqOegNhT+ELZJK6QgxT2bSUGWHE8hF4tmaDiwXXENlQGYXPgX53tYDaunrrgEGobdwlqgFv7ynxFoFs9GSGsCMPn3hrSNgFPcd/xkxeOVCL8mAybHk2DArMrHTwIGmwf6HGoxYEwBsHppUKWpjDNgxB9h50ILx/uTUpsKscG3hZ2B1Go3KcizIDjybSi0YWiMqm9hUsmI1Z6qvlu0Q5zoV0hBagbMTjBYpV8sRXMTR6Ygq4u/6VQKkgZjnb4tExb3ECrwYrbgEgEcyrZEUvcApgOYAsA97PxeaRZQpSD5lAi/iiGuyT2EGkS2EwxYmfeBxlIuodHShqKVIhhL8XfNhT+MmMgxbNx28Filw5VuJzSk/qli+k2lIIes3oIDqBSkBcektdKoKl3BDEewgVVBpGSPLLulq7YqC5+lrYt6F3986AS+eWeFxVszYJE21TZp+SpGCeCoqu6G0JaW1oApbSu1YLcEAxZUNmcW782O+R5WArPyPVSmwI7jYiWsDj6CGHsUGDVTkPI57Ficg0dd7D9youLxkQh/XGNMB2EkcfG5WVnDJcBPdA5hDQaMapLCjY3p2c945StfiVe+8pWz1yDfJQHAonuwc94F58DR1eJ50pja4MMf6b6ojQh/E0chAyYnCTHRRQxYlSpIsfBHAG5Xh+PUwMOJfjkgRMIJGCcwJQtnyx1HaS8wucsKqQAfdmzhL5uCozIFadgReKmSWxcifDlA7IgBK50yidlY6AFm0tLnwOSOPaEBI9Waitsy5SIuKNopGqUZsIhNZWaSiSwdoQdiOpjUBWDKTgTAiDtYW6tm5mrEhNNjUp3BUum7ETNrLThMVUwZDiyDis+oqQHj1K7VPFh5dlmuW+saAKHB8mIArIUJ7jlZAcCoKkgFoMIJXMori/DFhsbV51ClCtIhPhh1YLcEA1avMtrEeQsdnPLNyrpS7su2cK02xrw6AAtDBSJtkXoDqrOpsY0hM9zK1xAX4Q8VmPfKs6E8VIUI8j00q9l5eIFg0HxZmBMa1RkwGnuXIlZ79j7+/M//PH7+539+5vu+LGrxiZ3Y0Jw/L55JGQBmxjf4UqutGbDGhmLzRtSKKEMDpnoYmhFz4mAMViUFSfwEANstdeNlPbCE/sqEYdBE49zSDJhc+ANiJQAcUL6aUwEw0zTgUxdtjCuBF1NWGwGimECnIBkvxaTpXZZiEoMxXINUuAeB9HNLMpnVGLA4AIt8uCgtB8Diix4zWrWYSAQT+MSKJqoavkMTWHjig7fCN1vYYlZLg6rJlhg2BnrBKH8OLBBmtBdsX6glwlfVd4zasA1aSztEYum7cS3xt7gHttOqDcBIIFL6cT1iJXPlcAIGAkINwBYTSo+OSwMoGpdW2ILBEhqwcucQSStstCUDVrUoxGCiInfXgosRsyqnw7kES61Wu5YWT9tYGDZCXVFbtRgiSr/5hqu7NJQ/CakB4yYGkOdQBYD5sWpYANzuQHfLKBGe3BCFsQr1ygBMbsqI6ejxcMPd98383m233Ybbbrtt5vtBGDFgY0RAuutIqUQJKwqLiYpc8Q83oQGbNCnIszdoMMW8QGrASqcgMWMAulPMV/jRfeXYBxKIdIVJCWC2tCFoaWpVu2bLiqnYJFGKAeMcBvMwgQmTEoRWBx2MKjUUt7kQTgOA4XTRjjVhLpOGjIBw9Bx6pl+ehZuuZkU1AMZkKjlMaMBkCrKkMS+NVQsxqwUXPsqVT8ZCMifrSUF63MKca6E3twiXVTvekEB8sePgpCfvRYWyeSbH089cvgePffAu8c0qrAWLWAvbpAKIVjQF1oJdq1WrmTaTC7/rtusD4XACDyaIFbFPlQBYII43DUO3zunS8gxWggGTAK5LxqVTmCGPnkPLMTHkTi0A5hMbO+dbojdqxeeo5jXXbdcaD6Fi8QxHMOMVj4+fAzddBLQGqx1GAvi+aodUpc1boDYTCoB1Kx3vB1EvSUABsHqFOaIYQlzD/sPHZ37vJS95CV7ykpfMXkIYCiNWYieAdBUfLwtTDFisCrJJQW7iUMtmVgpSt9CJV8/xcSURvkh92eI/ELh8gh09p3TKQXlwGQaRDJgYtCujkuxFvG2J1Yr6ZkH4yBSGbpdhiYo/s4suGWPgVWWPxAAxWz20EZ1DmTQkjbktq+fQNfzSfmhU6ZRMBzBscGKgTSalWTytF9G7rJam2Y2yDBiLXYNM+/Aa2iGP168gjBZuAma20ULFRVOKXbd1bRybyKqrCjt29S4alovF+XnxvcpVXx6YIQBYnztYWl4q//cRPQfLdkTVVugBrEJbK8mAOW5chF89dTWBLYyFAbSJV9p0EoAG0gYltQBU3DpAMWBtlGfQGIPWHrVtQ9yHqgCMC+H1tq6NAXdBeFCpihFyHnNaMS1eBRDnx/S5pmHUagWkzpcaop9lZQAWRKbAE1qdAUtsLAFwq1PpHnhhCBtBBMCoWzkNqy0gTEenUdm4fE/MIPBFOy5iw4MpGrN7kYi+SOcb2YnE5+ZGhH9WhPYBy0hBJqog5UTn8gopSLlwc9OVFRpCvO1YtLQAnTIPE1iiYtGKxNsHl0ouvsq7SaYgTTYGgZhoSzFgSsSvWDiniw7GOFLSowUQ+iklkrRac+hgDAV/SwEwFlswFANG/fJWHAp0Go54DvIa+iWbkSsgnUxBiomOluyMYCSsNASI9Cv21IRkQ+t6aEGCeZMSMLuLNq9WTCFYCwtbuzaOjgUA+9y+O0sfHy+mqGOkGmdeRl6IAVo4eqJa5ZkpNzCW7aIfVje/VB5ayRRkddZAMGDyXTYq9neVqWQxHoULexfj0oxworhIArBOBQAWfw5t28AYdmX9k8VEVwbHNKL0WxX2x1c2Fm3RxgeorAET2QkHlkllVW+9Xo4wHYTURYuUr5AHEKUgYQG27MVYoaG31jNKDRgzKwKwgMMhnrBJAkRniKqV0UzZ6zjoy+f4+X134trbyhX4qA2NqDAnCMx2ggEr2mQHLOoGAEDrcxsAdhZEpAFL/7kRT33JiarFhhVSkCxqgQMIHy1vAMsoX7GkLCAMKgCcEY7hmBT3niw5ULQIPwIvDgQjFJYBgdqrxoRBCez2HLpkhLuOlx/oVkw/ZbZ6MAjXXl6lUpDTHQkAdKlXfsGJ6RQAgNhdLBiT0g29g3i6AhAASk50RsnOCAnWQV7DZFR+shUnMpEWDvUZsDG3YBoU3Bap4IlXvopR6Xa2dR2sMXEO/3jdLaWP57EdO1feS1Wcv0P1HGz80mMuwAAuOhhXmmQN2ZWh41hYrQPA5CJtJWwoqqcgJ1JTCdNFl1YFYJIVjzFgHYwq9IKMjSdqAFYbc3SMSdneqkxpW220bEPYolTQHgFR5ZpjUQxU+q0K+yPn5larpVNfVY5XBqAwHViUCEuSymnQMUJQGKaoIHQxQVBFVqBE+NyEYbfBQCsyYGpeE9fP7E6l44UGLNDzmgBgFQ11ZaEaMR0MuDiPDhnjRX/33VLHKy2fNuo228BkrbSIPogVUwAQZqzeIEpBNkasmz8yU5C6CtLWO80WH+Ho6gQXv/qaws/loaBX9WJjdwFvANugpT2wqBThm5QAdhvEH2LPlnZlF/mQxkS/EvxUZcAsg8JszWGOTnDn8fIDVZhnikGumve+7InnA6jIgMU1K7R8JWa8Yaw4oS7maPkUZFQxJXUGTldP1mVF+BGYj67Br+L/xBjAfAHA6i78EsBZRrRwjwblKyFNWc26tevoRbOD8qyBZsAMG6ElxhObVABgXKWNHDx85xyuvHQPOmSE246UB7KG7MrQdgwshdUrz3TayXLgG2JTNuqv4uJXX4MPXz/bbDgtlAhfbKpa6FIPI6/awu1xUzPSANDGCJOA4RX/+IMSx8e0rQBgd9CroCFTjDAzHNgGxQAt0KDauyh89UQxhVq4qzwH9S61Wx301fEV2KNA9fQ0bJgGwRAt/fff9oXbcfGrrylmh6Um0zIpmCl63FbprhFnwLquhTFtVWIBaZzZhwQvFe6hqkrmcmP6hTuHulF82VDzGrVaek7oQmwMV4bFmzsu78FcV/YUtbuA1y/NgPmM6U0ZADk39+E0RqybPzjyB0vawu/wCqxD3LEakACsD9Mg5VOQcrcsdrsC3V+4pY17T1VLQbKYB5ZqglyKLlc6BblgEKeLeWOCO46VH+hWHLzI+3jpooC9ZQCYBi8xzUqXjEtXuJB4+g8AnC56tLwIP14xpa9BTpSlRfixd0lpf7wK4Ec9x/F6NGDhBBPJZFJXAKBxf7n04aZiwDo2Bly8S21SAYBNdTRgnIBXWDQTvVUBLC5sQQcT3HRfuYpicQ1i4e/YJpYCxYBVKf2PmEwuNzQrK6cAAH//zf2lPkOlIE3JandoNVNgHqg5gerxNFhbBgB8fN9sBdp0RB0+ove5iohfFUNwwwYhBCPSglmRAbO4h5BYUwxYhc+Qz6HVbscqCCuAeT+aUyyDChZNvgf/60s/Fr9TND9KAGYbFKHRqtzeLG7E6toGRqRVEYSqghLJHlndSvfAC0QvSSLfgz53xbtRwdsvSkHa+jm0ZYX3o//r53HToRVwzvHHf/zH+OM//uOZ4xWj/JwrLsJfv+BxaHXnAW+gAVTRJjtUDcn1u9wBJn0YlMAyyLkpwieE7CeE3EgI2UcIuV5+bwsh5AuEkB/L/y/e3+dRFEUpSJNNpb4IhcvK7/S41rzE0PlkDVaFtiEG8+DDEjo1uwf4Q1wwb+HISsnFNy7CN5UHVmQDURixdhkGEcxJF2PcUzYFCinYpbEBAqGlA8oCsJiI3lEplyqal6RYFXYHXTIuDcDCaRG+BNLgvLQInzIfDASgJto9IUAfVAA/6jmO4i7yVUX8gYcxN2FSqgHYZFiFARPs0baeg4EEgVUYsLhmxjDEwlsFgAVShM/lc+jNzcMhPm4+WF4Hpgx1244RAbAKrANRImXTBbVaCGHoCj5apbMEIgasTaqlILkspjAoNDN/YVeMhTnXLL6GmfHQRaeKD5jscas2JB5twQorVqNKWYJjGBEDVkX/pBmwVuRJVwG8KDsRmAKADdCaAS+F82MoGTCDCAaMTODXSEEyw4FrUoxIW9+DHx5YxoFT+XNsJK0Q9y8w2tUAWMhgk0BvTDUQrvAcDObDlxW5Pkx43ECXRPPSz/3v6/CR6w/i6U9/Op7+9KfPHK9kCbbTxrMuP19vbqMqyIIUJIt5bQLieOYDgQfXMs5pBuynOedXcM6vlP9+NYAvcc4vAfAl+e8NjagKMn3ijKogpXjb7iZK98tQ1ABiVH830oCV1i/58JU/igQvc0YFC4ZYr664BxZQsgpSHu8TU/SOdHpw+RCnBl5pAbclu93Hr0EB2XI2FHEmUiw4bQnAypyDorkNSz2HHjoYlQZgaqJSVL0AgRzwh1KEX3wOJpPmm4Sg21sAAPTXyjM3GoAxMyrXrtqQO4xSkKYCYIPy52AwsWhu70Yl51XE29pOxLBhUoIBXLAK4CcMlPGk1PJJ8HHXwaOlP0NV5AoGrHr1XFR5ZqNlm5hQF0SyP1nFPNOhequaVHj7dcgEwwpVxdyfZcCetMfF0x9+HnquVXh8/DkAkBuSUWldKgsD+RzEOzAx2jArtidTulDbpLU8sKLm9i10HBsTWg186GbepgPLIMLXbupdLGbAPC3NYGYb7coMmGrtZcG1jAQL98qP/FAzcVmh2gApbatOQZaclzUDZonj62jxDCa8CU1K5We00MYYT3/4efp3bj2yhn379mHfvn2zHxAkr0GskWuglMA2is22dWU0jWUn5DV0bLPSuDpTsVEpyF8E8F759XsB/NIGnYeOYhF+TAMGAHYnkYIsHKDxZt7yeHh9oQErWS1jsAk8kgQv3UqC2xgDpkvOqzNgug2P3YXNxgjCAIMypfOcw41XqchF05FAtlSvL10FaSc0LwDKLRqB6hsXaV6qmMkGsmIq/h4AEFR3SSd8KplMAJifXwAAjKoAsDACYLrqqwpw4BwIxhgxsXCbLdFAOKjAgFE2AUwHC20LAUxMuIUOKX8fScxXz6BEMB8VABiPbyYA/RzuO3ai9HgQbbGEfUJ0HyswYLEUZMs2MCYtLUAv2dtetPbSNhLClmVUJVUi7UhMyaCBUFCvj92LLayOi9NHytpGT3xOF+0qDJg/VX1ndWBXZMAsLnShAoBVf5/j/WG39xyMiYtwvIrXffymUl1GWBABMHMqBamicH6U1aiWQYU/IpkgCCss+OEEAbFhGhSOAqJyPIz9EKOC+dWQ72KvK+bEU4ENcFZamiBc6H3RkgrQsoIqz0HNa6Yh3qUBXHTJGJec10383ite8Qq84hWvmDmezJAUUSWnY9JCBstXm+P4GgsIAOYYGJQstDqTcSYAGAfweULI9wghL5bfO49zflh+fQTAeWkHEkJeTAi5nhBy/fHjs4Zup/ck83tBmmrRlOgedhdOLAVZWA0ZJKvvRApSacDKpiB93UBZgZc2xggYL2eHEWu9oiumJEVcTgOmGLBYGhWC+VgalPAik3oCNjVAqgAw5cQvWEhZjSqBcCkmMFC976Ln4PJRaXpaTVRRMYUsGff6pVOQBvM1kJ5f2CLOvQL4Udcw4gZCGKIdkVehilI+h7FkwOyOAGB+Sc8ezjkM2QpJMT0DOGhjjLWSdh5xLzSTElG2XgH8hGphie+WAdhsiOMlFl3OOUzug1ELHcdEXy04NVJfMB10HRNDtKqnIFksBel00caokg9YlIIkmpmH18eca6I/CQpZYVOaoOqwO+hUSMnPOrB3K5v6WhDPwaAEEyK0dNVSwdHCffmueawwB4ePncD7vnUP3vzpWwuPZ17EvHRsA8vhbBVk4fwaephwE7ZJwWRj9bBKT8xAaCotg8KxZCpWjgfOizeXKjNwya6taFkGfrwkz7ckgBKtiHzQaQaswnNQRS2WIRkw7uJRO0xYJXcjPMYoA9BrJAA4llGSAfMjAOYoBmyAjmNW8qs8U3EmANiTOeePBfAcAH9ACPnJ+A+5mCFS327O+bs451dyzq/cvn37/XqS0TyV/rIIzUuM0rc7sGMTTWEGT1apEKt+ClKZX6q/D0AbmZZjf6TOwIzYo648vkoVJDOSFG8HI5wqBcDk8VMpSEsCWS8oxx7p52ApDZkEYGWYA3kOZowBc9iotJ2IH8YMdWPXAK8PSsr5gJlsgkAyYC0JfrxKAEw8syETnzGM7ZZLhXLdlhowuy3OISxphTH0Qljw9bvsWoI16JAJ1iblRLtxLzSDUgy5W9ECQjEvSQDWxQjLw+J3UWv5DMWAVRd/k1g6XACwaNGsxIDpyuYOWnxUwwk/Yh2UbqbrmuC8uH1LYjzJ4zsY40S/nLkznyouok4PDjygAvtjxVhx3xQAjFXywFJMpIVH7JrDaujodHY5WUI0Ny92bKwyB7wGAzbmQoTP5JzAJtXGdEAECHVMijUejemQ8cJKeTWeLKeFq/duwS0n5e+X3NT4shJUATBdTVphY2cw0eXEoBEDdsk8YBolYUa8sTyg10gAst9vGQ2YD25OpSAnIgVZpWXemYr7HYBxzg/J/x8D8M8ArgZwlBCyEwDk/8s5td2PoTVgWSlI7kuDOBlOD05YhQGTJbpT+W2bVmHAPNksFfrlqgXAaFTJqSrXyvmAxYxcAc3CdcgYp0oserMAThxvy/tYxjfHkFQ9AMFGxnbcZcxYVbpCNROH3YPNJyBhSQYsCOESX4td9S5r0odByzGJyn8KAGC1wUAQjqsDqFEoqoOGpKJnT6zk3TQI3M4CgPKu1csjHzZ8GHKy7rkW+rwltHQlGTAaSyULBqzaNQSeKrtPsrFtMsZyiZJ3P+SwiS8NRM1ampc489J1TVE5VlEDZihzZap0pcOKvSBjxwNiTE5W0XXE+1XESCrWQofdRQujUqk7IGLAFBNpSD0hr7BwOzzmq2c4CDjFv3zn9tLHUzUvEoLLds1hAFdvaMo8BxbTHm1pSzf+YJwAkUXjWjGRlkEByYCxSmPag09tWBKArfIWMF4Wn8N5oT42blC9a6GFY7o9WLlzUM24ieXiNc95WK0NCWWimtVSKUi5qTJiu5G8xzFbINUVJteMwbVo4QZbbKqCJIADzt0UJCGkQwjpqa8BPBPATQA+AeB35K/9DoB/uT/Po1QoJ/yMH9t8dqJKpCCLBqivhKKx/DZnaFO/tAbMjO9WdfpNsUfl2Z+4fqoOA6Zd4F1RwTePQbkUZJDOgNlh+RSkEeslqT5DPYcyKchot5xcuO2SaZPAT5kkAMGAUVKKSUukfSjFhFSrAFT3sR/KJrUpVVu5Ees7Z1KC9sI2hJyADMvtg5aHHhziw5Qg9hcfvQtDOOhUSEHG25YYUoRPKoCf5VVxv7odyUDa0ftcCoBJY2Ru2OjYBhioaMRc4TnEQaRIYzrVNGCMgfIg0nA5PThshONrk0rmyrozBQDM7QRWD6EnKyD/tcCFXLEWOuR4OjWYlJQ1SPG43JCYbQHAhv3y7I8Vs3VxLAGGlyu0laKx8bSlY6PPWzCD8s9Bz82mg8WOnWrmWrQ55NLPzTKJ8K8CKo/pAJbUgBm4l20HJqvA8JQEYAUMGIs0lY5JsaKMhUuegyeZfWq5+NlH7tRO9v3V5dKXoGxd4iJ8eH0NyAojbgoMxFKIwsurkAELQ+HnZiYlLgKAnZspyPMAXEcI+SGA7wC4hnP+WQBvAfAMQsiPATxd/ntTRB4DFk5pJawwnoLMn6wU8xJpwCL2yC+ZgjTjKUh5vLJwKAXA4sJlOUn8h5/ahR09p1IvSA2gFi4CAOwhx0qlIBXVry0cTAegJqygL6+hjH7Kixg4IAGEy6QgZ8vupQ6tpKdb4E89xxgAKyvCF6nk6BomtK2r58qdhDiHoWLA4FYCDtpziJswDYpuu40jZDvo0v5Sh68MBQOm0riv+dmH4+ILdmGeDEprh2hswTANggFvVQJgJ1fF78715P2PpeSXS/RG9WXVFzMctB0BVsKKpfvxityeY2IldED1wl9i0VHu5zEGTGwEOJ7xtq+UOgcSevC4Gf29LQ8CTt2FrgRgr/nYjblj0+RetKECgPZWGDxEh/WxMipjnplMQap09uryqVLnr/3c5DkIIb6rDTzLBGUTPTe3LFFQoVh1owQCU1WQpt0SAE6Cj3FMFlBmgz2RPmARAKvGavtEMNKORXFnuEN8/9TdYLx4c6qr9E0bjklxXyj7q64We8EBURUkNWUxhExBvuuLPyx9CepdiovwMV7VgAwQPMeb3vQmvOlNb5o5nrCpzbEGUAM4VnEVJJt6F+MasLZtnnsMGOf8Ls75o+V/l3PO/0x+/yTn/Gmc80s450/nnJcbrffnuRb8XFTqxFOQXT3IgRIpyFDZHyQX7g7G8EqWK5uyaa04ftrCoRwAC2CAGiZgWIDhoIsRuq5ZiQHTO+bFi8BBsJcexVKJFKSqmNLpBkKA3k7YwyPic0uAQLXL0uF0tYi/zIIRF06L42UxAyu38DJP/C1Vrp1MQZYDYInnCGBCWwk2tTDkuzRgAoANKgrYEylIuUCdtC8AWbobn/hh8YS9NPThwIfliIXKoAStbRdiFzmBtZIAzIhNtoZMQfJJH1+4uZyNxJIEYAsKgLWEleAi6ZdPQUrBbscW9/GEb89of4qugYEC1ETHMbHGHG1CWmbhjzdgNikF7A4IONoo39lBpSC1BmzLg4DREhYQXUfe5syc3lgu7AEA7CYnyqUhg2QquNURC/9ayapenzGhGVMMGiU4yeewnVSwRAmjOaFlG+jzlp4TyqQgQw3AXGzp2LqRtDK0Bcr6gMkUpCOd3CuOSR8WLCqqIO/QAOwuoQEr+PuRP6IL26S4O9gq/r1SriODcsI3bDcqigHgDctv7EwuLCCUCP8IXwTWDsMmSeDzxCc+EU984hNnjqcxXSiARIGTaxr42o9P4Af3ZjOjWpYQt3oCgMkauo5xbmrAzpZQDFbWztXifjIF2dmOlr8EE0Hi+KxQpo00noKEELCXAk+yaiucEuE7rAIDFsb6xgG6jY5FaaUqSF36bzog83twiXW81KKnyr318QAwvwd2/xCAkjYUPObzAgB2F23JXpVpyZRo5g0AcxcAALazclW2TGv5ks8R3kC/O0XvgjWlu/FoSz/HUiHv40iK8AeoZuEQMS9mtFvtXoiLyBG8/B+K29csjzw4COC4Lf09ungRtpPV0i2V4sbGJiUYchcWG+Mlf/+dUsevyBSkSoOitQhYHVxsnCglwlcl6zBs7F4Uwu/lwEYwKp86U/0wQYhIccCFIdvwlNKAaSBswjBIrKp4VDptQ8KYDxggABiA845dF/2ZnI2NyT2E8fG4cCEAYDc5Xk6IrzY0cl5TxsLLS+X21L6v/Nxsea4cB/gO7CHlZcHxOaElPbSUNKMMDk4wYG1b6wFH/QoMmGpFZNBYM+0qAGwMn9hShG/gAJcA7OMvhcuGhRowi3na3NmRDBZvbQGW7y31533fg0E4qOnKhuQWQk7QqdDdQlQVRyL8e/l5AA8x50dzKyHAN77xDXzjG9+YOT7R5xfQm2OMV/Q89cvvmD1OBZ/JTkRzc9s2MfLDag3Sz0A0AEyGei5Z49WCn6TqFy4CBcNOIpy3y9pQaAPQ2GRbqhfktAWEaodUQf+kvGo0AJMl6wYlJX3ApjRcALDlYjyXfxVblm8qPFxNdMxI7rjNNbFL80ukINMAmMNGoAQ4WOAWDaRowGQadRcvx7wEqmRdM5k9AAQYL2s2qehdmGbAPKOtF4ySJyGOk5Ykg4oVhHEGzJIL91p7D7aQPv7C+t+Fhy8PhAbMcSIAppiT9uhwxlHJMHnkq2dQKkT4KO+mv9KXi5sZY1MXL8Zes9xmQNiJeOCm8PD6s19+BJZ4FxicKPX3AVHNqsZjTwIwm41AwFAKP01p8dTC/cqnXgA/5Fgr8vHiHESKv7UGbOslAIBdX/r32ArBIuUtOhb3k+NpXgCwC0oyYGyquGjP+cJR6OPfvrWULjSYKDsR6aQfMBzg27GbHC9RWi7CiKVRW7aBo3wBbYzRw7CUMTLTTdVdzLUsoakEMBlGLFyhqarSgBlE97it2k5JOek7JsUENsKFvQDz8bP8a6VSkKHcDNjSOZ7N7ykNwKJiCtGTEyBYRSfBpBaFxT0ww9FzygEunAt64ySr/trXvhavfe1rZ46nbCo7Mb9b/H/5XpwssRmI+7mJE2oDhMqiFKmX3WQ6sAaAyVADNWvnanEv6mEIAIsXAwAulDu1wgEqF37qSADW2wkAWAiOl+sFqSoQ1WRJDcBswa6YgtSeQYAuWTeNcqkzzYApDRcAXPQkAMDTj/1d4eHaMyh+/MKFMPqHYSIoVclpch+hETvenQcZLWHnfAsHlop1I5SJe6D93LrnISA2dpUsxOWy5Y8G0oYJzO0Clu8V3QFQvFs2pwoJPKONdhUAFtMOAZIBq+EcHgfjVz7r+QCAXzC+CT5azj1cUf1mAoCJhbszKqc5MVRRCyEwKcEpLrRD20qmnvoDeb/iYH7xIuzGcSyVAGDBVMVUyzJwgG8HXSm3YAGqG4B4Bh3HxBoXTNo8BiUZMGUHEvmAAcD5rlgk7jk5zPenYwEIeGTkCgDbLwWe+hoAwPO33wEgv8PEDABrbwGz2thNTpQqqGBe0ti4tVUA8dboCP77524rPN6XKX1IJ30/ZDjAd8AhAdA/gv/7tbtw8auvyRRgc84F+6M0ZAbFIQj2aA85VmhgCkQ6NtMRpsCBK7z5wtVoTiga00S2IrJNCiIrQWnFxu6ijQ+FY4m56eSLvg7M78FTyA8KN+lWbE6xZQow7O0GTt1dyg1f98M0XZ1CPMi3CSBcMiwE4DENmAJg8+ODpY6fyU4sis0xlvbj0LJ4T3bNu5nHzzBgUuKC1cNoO1IvW8Fj70xEA8BKhmihE1v4F5UAXbygRTstbX+gFm65YG31D8MLS7TRUfqrRBp0G9yJYOBKpyC57OMIyBTkWgUGTDrhx4HoU1+Nz7Z/Hg8b7yt0Xdau2WbsPs7vAeEMe8jxwkmGMZ6omAIg7uPqIVy8aOHeEgyYEW/nBACUYtnZiQtKMmDqGpRfjjiHi4Cle/QiWPQuqN53Kvr2dmznJ7E08PREkxuxpui2QYVrdYW2I/Hj1WS5Zc/D8YnHvFt8//Yv5x4eiV2je0AXZerq1DdLnYLJIk2lQQnulSmXi8jRkozwlN4DABYuwkXsAFaGxcyNEh2r3XLLMnAv3wFjeLw0cxEvzOm6Jg7ybQAEe1R2QwTEe6uKlMl2WwDIn/vf1+Ex//ULJY6PMWAA8JP/CejswG8sCBPSfAbMizSZAEAI+JYH47nGt0qB0VCzRxKMt7cCdhdXza/g1iPF6dzASy6angRgAIBbr8FfyBY8WUBKaPmipuyEEBw1zgcgAFiZBsxq4bblNYzauwAgcf1F+lQim6JbBgWVz7FKUYliwEyZggSACSPAQ56Oq/Gjwk16/F10LHH8eOfVwKk7ga+9tfjvx7R8ah67h5+nCYaiYIzD5qIRtppTDvOtADHw0B+/G60SzDaN+bmJC+mJ92n5Hq3vvXBrO/N4HkwBMECsD8v3agasbJHQmYoGgMkoWjRndopzF4ARE79AvwGAF+6QlMCQxvPTnR1YnAjGoLjXmBKwx8DDwkVoDcTuomwbngksoTcBtNGdSUnJXpBj2Woi+drc2rkaDp8A9+XrhxRzYsTBy56rwamFt1nvKJxkPOVCn0iB7gV4iMs6qzhcArzQMNbOScaKuwvPot8BDhdX/HB5DdSOLfyLFwNL+zWwLWbAktew4uzCFqziGf/t03jSW76Mz950JB+QyxTiBCZciwrTRM4Av6x1QZTCtGLPcnL+lQg5gXdwX+7hM5WkAIy5C/Cp8CfwmEMfBI4XezjFQahJidCLQCyaqyWYl0RPUBXbHgIKhn+3/OeFx/uBKFlXk7VrGzioFv6SaRuLT/Q1dJ3o+N3keCUA5hNLsKcyBbnNitItuZ5gYZRKToj+KQV2PBztgdBWZoGHUBlXGsnxgJ//C5xPlrDn0DWFl6D1U2pjSQiwuBd7cAyHl4sX3dBTJqjSiDVkuJldiIBTsM++Fm5BccwkCGGTmPs5gJOWyC7sIcdLeaqphduWesJ2dw4rdB7mWsTcFDNgnh5PpmViyB0QvxoDpnz5VPPpSRACCxeiS8agQf7cZqcwYEuP+n1gz+OBWz9d+OdZjAFTcYDvkKng4nvoSSNXbjq66jGACVz9YnQGB/Asen3hZ5jcgwc7aUUg59ZnXCbmh7z+pirDQhKbsguB5XvQtmUKcpNVQjYATIbWgGVkDmz4SeaHGji28Gg80bgZz6LfLc2AadEwACxehHkJwArTkJp9SrI/jpxky9lQiJJ1My7Cl9V7pRrHBl60WMRi3BYTHvr5uyVf6j2MeOpqx8MR/tRrcAW9E8YoX3/jhdK7Kb7oLu4FAOxkR0uBUMo87UKv4ge7/o344rq3Fx6v2mXoBQcQbOjafbBkKXgRlrWnWId+S+y4F33Bwv27938Pn8+rBtTVczYcy8BaVdPEafsDGXPdNpbRhd8/mXs4n64kBUANircGvy7+cW+2UBYQaSObe3rHblCC45jHkDu4iJSrqKXTWj4AeMwLcKt7BZ40uU63W8qKcMpzSDFgAIBbi4EHkASRXcfCAcmACQBWxdg4WTa/aBVfP4AohTkNwACgvRW2tyx+LeNclPt5ggEDYOx+LO7gu7FjuXhDothQy03Oa+eHh3FsbVzIZgaqqlgumkHIcRyL+F3/P4IyD48KbxbfzwBAY58l288A8O15rPA2XmJ+EtakuBhAMydyTC+2bRwhO+D2owrC3AwBC0Gkn5tlEJiUoo8WjCrtwSSDZsoqSHVtaIt0aCfMT83H30WVwvRCDlz8JODIDYBXsDnTYzoaT/eqVPD+6zIOisKTVZSIMWAAgGe9Cb49jydQ8RzzEvOUJe15AIj5/a5r8c7LbsHebZ3c9yl6jlPZidVDmLPF8yvTH/VMRgPAZGgNWMYrYmN2ovriVe/GiNt4Mr0JRWs/ZR4YJ6BmUke2e/m7uJLcWgwe5KKbSEEuXAhrcAQ2/JI77jHGiKcge6LCpGwVpPKqmZrsuZwkMMqf7BQDZsVBKAB6sdCRbSuY8L1A7rLiz0Fq8a5augZeCSd8g/kzg/zA4uPxz+GTwPdfV5jGU+kKIw7AZOXZM254BYBiEf50GnXNFQAsrrdYydMxhRGD5ZgUy0yKfssKyDWDZiWq7RZaFpZ5F3yQ/xxJSgoSAA6QnRiYi8CB/ErGgHHYJNDjSUzYIg35++ZnMDpcrB2KG0/qMB18feuvwMUE377uC/jI9dkl+OEkufC3LAN38Z3gIMCX3wCsHCo8B9XMGwDatoFVdLDKW9hTmQGT/V27AgDaw6PoyZRJ/vHKzy0NgG2BpQBYxtieSPdzTDNgAG6kD8UF/RsKdxNqPFhxRnjLXmwd3Y3/YHwIx9by08ERA+YmzvXb7OGYcAtXEwnAMkDkJAhnrqFlGfho+FPYTlbxE/38dDoQb+YtnuWWjo0fe1uBE7dDGRTlzo8xIGybFJZBcB/fCndQTg8p/sAEE9mbVaUQJwETlYwAeiw/nWvHisQUAzYJGLD7aoAFhew+1ynIaEzfxoSeD+//1cLWUspJH2YkwgcgJB47rsZP0FvEZXKOt7/97Xj7298+8xnmdFcGQOgZ7S7MG/8BC20rFwgrAJbIsCxcCHCGPcvfBYDSHR7OVDQATAYvZMCCZAoSADFMfJNdhifQm0tT1Ik/8KjfBAC83Pzn4gl72oUeABYvAgHHXnK4VBWkKpXWDNb8HqB/FC4p6cYfCPbImLpJRAGwYf7CHcrdrmknF2666wp43MB5KzfmHj9Rup34wt/bCVALj1z6Ih4V3lJ4CSafJJsPQzAw32KXgQyOASfvzP8A3cootuA87OeAi56MnSe/jTkMCt8FG14CRA7bwgrjGTGaXlUypUYwAScmOChcy8C9UuyK5Xvyz33qGoQDe/R3Fto2TqEHjPIZMEyXi8swCMXh7sOBwzfkHh5IDy71LisLhU+FjwcAzN/4t4WXEDeejMeh3hUAgLv2fQV/c93d2eeg0xWSAZMA6ltPkcUkd3819+9zzmHxSHvkWgYAggN8B37F+BoWJyWqQfWYlvextSi6Syzt1yxGbsSAePw5AgDaW2F6K6BgmayBaiyfALEyDlh70Q7XgFGBI71cuO04A3bl7yEw2/g14ys4vJzPvCjxN516lyawcTu/AJcQkQbMSqOKOcHTNhiAeJZvCF6Ae9gOXD7Zl3/+ABBMEIKKghoAix0bN7OLcD5O4t8b/yz/fs6YjjHKtkG1prEzKOfBpc5hIp+jK8f+yAs1AOvkADDxLvoJM1tAAjAlZF8reB+D2Q3N9/kleHvwKwDzgUPfyz3cU8/BcJIMGIDx3IOkWwBHEHJcccUVuOKKK2Y+w2BTXpsAsO0hwJW/Cxz4DjrEy18ng5QU5IN+CrA6OP8r/wkAL1VNeSajAWAyeE4rIsY4HHiRg7sMgxDcxC/Gg8hhsII8ucEmmGAK3V/ydNx28W/jKnobgkmBXmK6jyIAnP8oAMDbrb+EV0brIF2zte5n8WIAHOeFR0szYB6xMT3Xu60OBtwBG+Qv3MpF3nKmhJSWi+NYRMvLr7gRuywfiO9wKAX+4NsAgCeh2MPKYEkLCEAAsP1MCHexms986IqpOACz28BPvwYUDFfTW/PT0YzBQpgAkb67Dd9hD8W/Mf8Vl5H94iMLABiTC5ZjUtwdCgA2PPLj3HOPHw/MircX2xaWeQ/GOH/RpeFUubf6PoVgwAqYUKXlU1XF6hz+T/jL+HJ4BRbuuy7XS40xDlMZT06xcIG7BT4MdMLl3Mk6KqaIGDAAODj3WKC1Bbg734neC5nQHlEFwMTzemPwfMyREa5c+hR+/73fzf2MVFuXxb3AqbsTjFbm2IyL+KcZsNYWEHDMo595vBpPMxowAGNTOqkXADCtn4qP6S17cfzJ/xXnkWX0D+RvqpivjI1nQeAS72GRRA2p02LiR35uKlz5LL/OLscjgxsLWW1VwahPv23j3eFzcQPbi583vin/ft7CH7MTMSgsg+Iefh7a48OFqXAdskDKNEikV/IChBKAzfNsAOaHorG8AvJaxB+E4l0GCsekblOX2NAQ/G3wbGHlcGc+k+h5wkeMSF+/eDB3AQ4J0MYEAWP44he/iC9+8Yszn2FOe22quOhJAPPxkPCufKmMBJE0/i4tXAg88w2ga/fhwfQoTg4aBmxTRp4GLJDtMqYnKkoJlngPlHBgnJ+jJ6EHH7NphRPbH48W8UAOF4AHPVnHFpzzH4H+T/wRHk4PwOqXKPX1x5ggqnJR+qnz2JGSDJgQ4U9P9l3HxBJ6GCzna8CUi7xtz062a6QLc7Kce7yqXCNTiy62PhgH5x6DJ5EbCk1QjXg/TRmUECxBpPHuPlCwa9Ul61MgcvdVYMTEY+iP88GsYo+s6F0yTQMv9/4QALRWwjZyhmY40Yu2Y1KcQg9rvIWV+6oCsGRBxXzbwineg1XwHGZ6tqnroBRDY65w0Q6kXkSlIOPv09fZI9Ab7MdTX/+Pmcf7TKSi087BtU2s8C464WpuWl+Jx5VeRC3aA49hvOPRwPFbc69BLXpJBgz4Jrsc+9iDcDW9FV+8paCCTDFg0yn1pf0JRivTuyiMUsnTix7awgl9C1nLHNt+EAiNTwoD5jvlAJhKR1tWcm609j4ZAGAf3Zd7fOglq4ofel5P/2wJPcxLH6rsNGoIG36i8k09i7v4LvQwLGzTJebmaE5Y7NjwYeKz4dW4lB7CAtYKFv5oQ2MbVBaV7ADlIXCi7JgcYyz1uS3ZmWHkh2Cu6PAwx9cy57aQcVmIkGTAvIABrQXxSwXP0Zi2gJCxii74woWimjInfC8aT8qC5WHny8bsskvFAvoIQo43vvGNeOMb3zjzGTNtsVTMSY0sVuCXYCLpVIYFF4t38adbP8aJtYYB25SR5wMWqgVj6uUwiABgAEAKdhgG8zBJQfeTnqSIC5gXteixKYo2fNgvAgBO3fyVQvDBQ0VzKwB2MQDg0smPSlZBCpGkMUWBdR0TS7yL7958R+7hinVICHZlkNYiJmsnc32PPF/oPdJ2y6e6l+IiUizEt6abqgMwKIQJJ4Ab78hOWwHQk21CZwAApoOJvYAtWMsFYMGU7xEAWAbFEWzFvWw7rqJC/2TlMmBjDV7Eblekvnbe9vfA2hEwxvGtu3LYyLgBaCxd4JgGVkgXrr+cyxrQKc2M/j6BAGD+EPCzGV2lAVMLRhw8aPNGP/v8VRshZTwZD8ekWJIALM/YV9knqOfYloven37qZnzmLg9hAZvra/ZIHG/J1BMAfJc9DFeQOwVbmxfaV2/KX/DUnfgNFhUCZPawyxXhxxa9jLGtNJlkCsQCQGAviC+KGLBQGJCSqTnBWRBVa7RgXlTmmUpT+Y8vfjx+8yqhPVri3WIGTG3KYnOCajmzIjdVRewPDZPib7UWXM8uBQBcQe/IZ7UVEOZCA2YaFHcrRv2dTyzUTyEMAM4EADOofheHXgjmyBZb6MPPeI6isbwfmxNiAMx0AKsDFHj7pVU2q+CtLcCwILsxpan8xB8+Cf/4YiEp4NJXbZH0czf6M22xVLQVC7iWX9QRJFltHdsuBeweHmUeaBiwzRq5wDpU7TKmUpCUYBnC8+Ur+27LBQ+UJXdZ+u9KipgXvOAqvx3S5Mtl7Lwcq7wF8/D38FdfLdAv6UobOVl3dwBmC89e+gDax/bhruMFZdOy39m0y3fHMRPpgqxg/hgBp3Cd2UG2sPV89NgafnhgOfN43x+DSpp75mf2AubJEL6fv8Mx+FQvSQgNkpqsB0sFxoMZ4AMAPHsBi6SfO1l7aqKKATglhP8hfzAeToSOq8iGQgFxlfpSiwW+9uf4v9fdhd9817dw7W0ZDEwQ1w4lH+YanYfJvVxLC6Idq6feRUowoJLByFm4fW0nMsuAqQ3NFpLNWijBb9pu2TEpltFBu4ABi4opxGZAsSacA8u8Wwg8fFWRG099yYXvdr4bDvGxgyznfoaugowz65f/MgDg9/BxKAH4IIsBSzRVz2HAMtgbb7p3Xiy4ZF6KGTAPk+nKNQDt7gIm3AQtSGezqeew2LHxuIvE315BF3MYSh1bHgALEhoyJbTuLAgw/6Xv35rbB5CySWJTdvkuYQp8SFa1bicr+RmCWE9PxxQM2Pf4pfjxec8BwAv1U+o9GMsqygQAoyZWeRuLOc8xUGxsjBUHgNuPruHLtx4FWovgo1P48PUHMtcoI83WRX2+s1io740zYADwqN0LWGiL89EMGFnLlQVYfMppQEVLsYCr+UzktNemCkKAzjZsMwY43mjANmnkLHiqUodNpSAJAZblgvHdW+7Ef/9sdvWWwdJTkOrlIgU7FLXLmk6D2qaJI3wLtpEVfLGokXEwkToDGl3Ab30QAPAM43o88235wmMEwqdlerc9mARYQg8LyKf6uQSArtQoxMPobsE86ec6FXs5O3bfWRCn2M+fKCzVsiP+t4lIxw24A7+fX0lIc3aKvr2ARZLPgPkp16A0eSf4PBYkiM3fccdTkOJe/knwIiyf9wTgji/hpkNCL5Jp5xCMJYAjM+LtoTEnv8jeEOh2TuY0kCXoq+NzFm4lwld2IvFzOAUxnhbRz2R0VRuh6fEICCC1zHuSActJQQbJ1JdBoxYuS7wnLARy9Dtx36P43wYkgAN0+iwzlK4zLivYdQXwi+9AxzuB9/2c3BRkgYe4D9g0cy83dgskWwOm5jU6zeYCOvVVxB6RcJK6sTQMihX0YE2KRPyqqGX2Pi7xLigROrYsFs/zpZ9b7BqOy8rLLdsEC/XeL30f/+2z2SnlaVnC5bvmce2rnopluSlbLGC14629HJPCNAg4KK59yP8HEAO4Y1bvlDxeATADJqVaAzbyAoScSyYwG7wELLkZUHPCWz9/O373PdcDrUX0l47jP330Bnzl9vQNZqqvnvp8p1jXqTTMae+SKtJalCnIrIhXFSd/0AYMBz2+lskCAlE63LRmMyxoLWKRDHCyqYLcnKHGVxrzoNIV0+DHoJF2aBFrOLqWnXYxmDdTfQcApmVjlbcKU5h6sp4aIJZBsIwuFtHH3SfyfaBUJWaC9Xjwz+BW5xH4SXpDsQ5MMmDTDct/5uE7EumCrODBGB4sPcEmzq21iAUMMMrp1RXoBWN2xx7IBaModWRNudADEQOzBLFw5wXJqAAEJAuHQS54Ul5oNMY6qOexgg7myRAGwnxbk2CiuzJE1XIEx3c/Azh1J9yhSGd37AwrgzDSWkw3fR6ZCkAtZ/55I0zXi1BSkQGT48mInYMCLwtkDf2Md0ExYNOSACBKQfbYKia5DFiyhQ4QCfGXdOoq7xq49D3KBmALBeNhprm9ir0/CQDYtSaqSWulIF2h4ZrDMFM3owFYyrtstOdFc+ciBiz04E8XF8lYJT1thZEVigGLF7U4GgjLuTUndaWAdLzyTVWEX7hb9BJcQD/7HkLOzVPv0nlzLgZw4XEDC2RQinnRNhRyQzEgHWDbJcDxgupsOZ7G3NQbAZMSDL0QnAFraKODcaa3XBDyhD3PTAFPexFcMlhZfpE0Y0wDgGfPA8P89yD0VYu2FHlJO9oM5K0xmQwYIUB7C3osX4un5uYZDRggjudrpdprncloAJgMtWimrZ1KsMunqHrG45NtPvgx0kzmINiPZd7Ft3+Ur5+Kt4qIByEEy7yLBdIvfrkCIcKfdrLfFz4Yl5Bi3yPl1jw92e/ouXjYRbvQxSi/4kgyYE6Kvom2t8AiIfxRNoumdllGygALSwKw6TZAALQtxwrvFLIWJPTEwkRnwY3vLEoGLPt4f5yk6oFowlTvUqGVRTDRLF6cTRz2LhbfGwkmtJPlJRVEDNr0s/RN1Ug4+z6QjN2yQQn6pAwAUxowxYDFAJje0PQzvdBUBWIaA+aYBpbRxRwEY5CZytXVrNGC0ZoCUHlpl8jCIQ7A6Mw1lCnI4NO7/p5gbjqBuIfFDFiKDYUjnkMXo0x9Z+DNvosqOq6DVd7WC3dWpJpnylijc3D8gt6ecl6LPweVTlWygIW8+yjZpzij/OGXPAH/79MuwfYd4j4ukj62dtNBIiDm5mntkRDCE6ygi4VCBixKBdsmBaUElEjvsvbWQvCijh+xyJevZRsYeiFCztFHCz0yymTApjsazACw1iIMWViTdR1mDgM2sRYBby0qvkm7BN3lZHZzTNpKx7aGgDH89V//Nf76r/965vcseDNem9E1bEGXreZqwBQAM1OKvNBaRDtYKdRJn+loAJgMzYCl/Uy1aZia8DnnWEUbISdYIGvpB8swuD/jwA6IxWcJPXTZar5Lr56sZyeSZd7FvNxd5L1gqQwYgHtGLbjEx5xZLBpONPOOhW92YRIGnqMd4tKJP40BM7pil5RnZRHmDHImhZ5FJqIWUhgwyeiVYfGoKllPKdYInAWxWORMEr50pI6zeGrxXIqB+SLR77TrNSAYOAA4dJ8A05mu0+EkxoAlp4DQkuAjp3LMYOlpWEoI+iUYsIAl/dzigDyEgRWpeVH936YjYtBm3wPHEhsaF75oz5IxHtJMG1X12RLUNWS/S9oSJScFuUD6+a1wgjECmDCnU/KGBThzcD0BXoo1YFaCRQQAUAPMaqNLRpmsQVSIMHsf246BZd4FKwAPNJzMVBWrGBhzaAfLucen2boopmcpdh+zroGpFj2xa3jcRYv4o2dcisWtohBgkaxhSycPgPkzxU0qlnhXMGAlU5Cqetk0qEiXtYsF7Or4MTP05rhtGxh6ARjnWOMtdJH9HPV4kATBzAY3BsCyriPy1ZsFQKu0eDyk9siVYdou+tzVTOJDH/pQPPShD535vSxWGwDQ3iJkBbnrm9AYG2aajmwLWuFaoUn2mY4GgMngyGbAslKQjHNwUKygg0X09WekhclmDUABpVkRC/+BvGbSgdKhzb7gKgUJ5PSEDAMQHiYaMKvoLAoH7h1GQSubcJK0sYifnmROwlF2Ck/57aQxYFZH+dVkT/hhStpIhTIsLJrsRPPh9BTkMnpYQD9XAE8yiikAAcAcEgB+9n3UO8XYNUzv+Is1J2NdSBAHs54t0k5KwJ452QQRgJt+FxQAO37ieCbzQsN0CwiDEqyVmKyVhYPSkE0DclXQkbUh8YN0WxhAMGBxWUCm6FdV38XaYsW1RwBw6HA2K+wHAWwSJixRVGp+OcbcZFpIALGq4hSo3N4Cx18GUJyC9NJsKABwu4cORpmLrjJGTkvp91wLy+ggKNjQGLEehNMxMucLU/rQrEXMF08+s+VSDJgycp09hx0LHazwNhbQT51zVJjc0yn9eHzsZU/Ezp07JZNZbADqk0hfa1KCMORCi1fSg2vIIo/Gtm0KET7jGMBFFyN4IcNtR2bnhoCpdPgsA9a2DcBdgCXBfNZ1ZNlQtCwD3zki/5HDhuoUZMrcbFEi5RUD+CHDJz/5SXzyk59M/A7nHBbSWW1xIovohMUMWNZYQGsRrXANnDUpyE0ZXDNgswNdt2mYSkGqd0GlAPPAdZoBKAA8avc8Ltx9ARZQBMDShc/q77eIBwdetiN+mD1Z/9tnPA4A4HjL+T0lJYM1I/gFENiiGjQcZzMnJFBGrrPHW10FwJYzj2c5AKys4WCqnYg8n1XexnzBbtdIaeYdnYOg2ser2UL+wJtlXnTzXCXeLmLAgkg0HF9YRuYCALFgAXkGnuOYC/2UaaItzuEdn9uHv/t6uiWHqRiwlKrgEVrCuHGcvfAqHzCSsWMXG4o1rGYwYF4YZhqIuhbFGhcebT0yyn6f5cJvJTRg4jwUEP5fn/w2fnw0/X3WrHjsOar77cOUO/4+RjlFJQjG8MmsJECcTNRKqMgHLIuV5nYPPZINwHQFYsp4mnNN9HkLLOc5AkIPOF3UomJkLaDD1gpkCbI9WQwIP3i7uP99Lr7XJaNM8TVPSUGq2NK2NZjP29CY3EvNLDz2wkW4ve2F2qW0AimTEnGMYsAKxjOgGDSZgrQMjFQKkrfQJSPcfHgVz3r7V/G/vphsdh8EIWwSagYsPr+HjANODwYPYCO744lOQRrJZ/mcR56Pfz0g3+GczS1P0fLpzzYo+rylNwN//ud/jj//8z9PXgPjicromWhvQTtcydWAGaGHCaxUKyndU5NVaJB+BqIBYDJU6i5dA5bmEhzpxvpooYNx7hiz+KzQExAarl07L8AiWcOBU6PsDwgnCEBBjVldz8t/7ioAYuHN3vFHZoHTPl6deVGuvUDWsJzXCFn2K0sDUKFkwPImbMImmXoRsyXE3zxHe6QFuyk7dup0MOFmPgDjHFYKza0WrzUIqj8PhKY181axfYdoSn7wULYpbtSOKTJyVSxU+aqrsS6bj4OXMWnD40bEgGV9RmzRtKa1Q7ZgsKygj6UMDRbNSFdQIlP5di9XQ+YHDC6J0nfTE+aS3NBcd8cJ/Outs1YaXiBEx2mCYcc00JfNyRVrkBpavD2bglzmYjOxgD7uzdgUKfF3/Bzi93sZgtXOq+pFOIEPc6YQAgDQ3qI9tIquIdWIFQCcnkxdZYAXvaGZFU7PuRb6aBeamOYxYIG9ABNh/mcEHibchB1Lwz585xy++ZqfQR8SgGEs2KTU45WObfYaKCVYRRtzyN9UWdxPWoHEo7WI+QIApxtZT3n7+SETGjAW6HvAOcf7vnUPTg1i82wYzc1WIgUZgnGgjzZ6GOHgkngXv3/vcuLPh7qtlriG+HjiHFoP2MEo8zoM7osMzdRYfPTuBRyeyOuaZM/tLCWVrMI0iGbxsp6DrozOeg7OHJxwmF8FybxER4NEyM1xj+dnOM50NABMRt744imTLRBVTA7kDiU3BZnVZgGA092CHhnh3pM5KUApgJ8R2wJw5xSA6meDBz1Z29meQVjDqTwAps8hhQGzigEYDVOaraqQzAvxsidrRXOnDXLbFL38SN6OnQUwwGbSuCp11OdCCzeZZJcqG8yHn6EXWVgU9/HQ0WwHdDVZxlMu1hQDtkAKRPhhBGTj6buAc51GBfJb2AQZKUhid8A4QTeHPTKYhwAGQJOpQ4MSvePOW3SDFPATj1V00MUI7//Wvfi375lt5+OHs/orFY5FE8xJ1jVExpOzVZADuGCcoEPGmRoupu1EouOVVcJvXrVH6DJRAMACL3NMo71VV0Zn7vpjDFjapghOD12SvehGDNjsfey5JgZwQXKANCDGQ6p7OcrpCZUsYXpOOX/OxQQWfG4IHVumlk9WvmW8S3Z7PvceAEIXmrnwt7cI+4QSIvy4JUnXNbE6DmaY+ZsPr+J1H78Jr/zwvpnjJ9yKAJhjYuiLFOQab4kqR+lxOL3OqMxAfH268qJFtCxDkARybu2Qcea7lGZQDQg2ck0C4bznqM7Bim0sVZiUSBZvnN2XlAk/N57xHOH0YPEJSI41DGWTdKsnQD+HRazlrvVnOhoAJiMv5cOnGvdGx4j/99FCt4ABEwAsA507XVBwDAY54EF6eE1bQADQ6H4xD4DFdlmzbUuiMuFTeUZ1QTYDxuRky3OqGPPSFXAEA0ZzJnyue0nO7nYtk2KNt3IBXFbZv2LABnKi8UbZlVvGlGljPIgrruHo8WwzVzVRmbFrUAzIWqygoygFqXRocQbMCxiWEBniZn5GGKUwp0X4LdvU73MWm2qy9MmaEiJ0Z063YLKetQ6IR5+Lqq+s8KT5ZhoAc01DMDdAbum+thNJ6SHIQTGAix5GmQCKaVlCdA5qPnjOI3diz66dWCCDwhSkR+x0Bqy1BWR4CoQgV8cWyqbsaZsiohiwTPAyW4GoYq5loc9djPor+NF92ePB4l62cFoyL7kVteEk1UZDsDhEvos5ACrItqYBgIdetAtdZAMPcQ3+jMm2Po/WPFrEizbhaZFi4bB7sSUkJe2kNrUvK9X7cX1lLAWpOmC0LQMjKcJXTCD15bieeh30eIrdg4++9In4/afsFXOArogdZ/qpGdxLWEC887cfiz/46QfjwTs6GHDFgGWP6WhuTmHAKNXPMbOQQPq5ZTNg4hocNsxksAzmZctD5NxcBMbPdDQAbCrSHq4afNPoPEpBuugiR78FZTKX/3JZfs5uMxjPtI7RIft9zWGQM1nLQc5TdtwKwKGPQdaCwUKAhxinmT4CYDJ1xXJoapqTroAjABzNuQepjbBlWIYY5HnHZ1WSxlOQAOAPckBkHpCW9yBPBxeqicpOpisAtfC30MOowAdsrCcaJ5a6CRjHgM5hUacgs46PzDOne066Fi1MxQrRcor5JiUilW93cxddPtWHcTr6Ml2RFYUMmExB9sgwczyk+bm1YmyiWjCydGjqXaSxHohqYu86BuDMoYNRfhVkKDWVGSJ8eGtoGywnBellavkAAI7UgGUcr9JGVqoI39T34E8/8aPMSzD5rKZSBXFlL8BcWYIwqE7V7UCA8Q4ZZQIHLcJP836CBKE5x3POYcPLBGDUVRvDvOzELBi/cEtbpAxldkFZUSiNbnzcxvW5jhyPjHPcfrSPv73ubs3oGn76xop5s38fECCWcei5tYthJhi3uJ8oRHjOI3fiPz7rYTh/zkVoKyYznyAA0gGYZRD9HLNSiIGW+WQzYAAEi5ZxDTSj3zIAzQLmgvkNiIyzPfdC+4Cl/SyD5tYMGG+hS0d5LhS5KUjF/lhBPnhI8+ACEFHMGGeL8GPtMmZAnGEhtOewGKwVpzAzFgwl3kYO+DBYDgg1XYSgMHIqCNWOPY05sQyCFd7KZdB0Jel09Z6c/NVOL8xprJ7F/gDQk0SLZV+DpupjDFgcEPfhiudYYEMRSCYz/iyCkGFgzOG88F7xazkeWD5pwTLIDJvp2oYW/WYZmZos/V02qGDAuNODP1jJsOcEwikX+uno8xZaxIOJAEHKFKVc6NPSTo4ZpSA7GGcbT7KJeN9ifm5KA6bOoUNG2D9IZ4QViIynMNXE3rZNEFeAn/wqyPFMQ/ToZMSmaJsxzElBTrR/VVoak7r5DJj2QkvpzdpzLQx4CxYJcX4n09AEJs/2bjJboip3beUU5vakH0/D7KpiQIyHHkboZ1yDAtIzvVlVFLCAnmwplaYnBAAiwYvh5+vYgGQl5u7FNk70PQyNruBjx8sA4gCMzhzvwYRlinutKhk/9oNDeKLcGAp5hzWzzrAgPUOjhja3uyAQ4CVLS2fxdCsOQgg6rQ6CiQkzT8sXKkuTFCNWkmQy3/++9838TlqP3ETEAFQQcqQ4GYl+y1mzjhNLwzIGIOUDNiAaBkxGnhGrbiw8tVNUwv1BkQhflthmgg/5cplBXhWkSP9liW0B8XJl21DIhrFpKUgAzF3AHBlgEmSVvEc6hXQAJne7Obskk+WkKwjBkLRhBXkALLsS1JYMmFGCQUur3gOgqf4wJ41q5bJ44h64LMcLTb5LdkoKEgA6vUV0ySjfMFCnriji664Xcqyhg65M32WVnHveCGs+Te7CZbimoQWzWa18sprmUiI0YAcGBu6+7yhuPJgOZFlKR4P4K6WeQwfpnSWUE36ahsy1DJ1KzhPh691yjHmJM2AqBZndzsmbuYaIATNB3R46GBekIEXVlpU2pqWT/TzNTgXHtXxpH0HcOXQwzvalS2FjVXTsqJhhVysbROYJpx/5YOFE/93b7s08nuZVFSMqcMoGoepdSmk/AwCOsOLIugfKzy1Le0TkmM7bGIoCKROWFYH5C7eINPh9I/k9CV7UhiBhlqr83BBpwP7k5y4DAOyab+nnoPS101kaBcCmNyRKrqLkIR1ks0fChT79Hri2gTFtA5O8uTV7bgYUqz1GEDDs2bMHe/YkEXnopRe66dBp1Gwh/nRLqbTjexjNpHA3MhoAJiMyYk15QZVoeGrhfuyFYpcqRJIBTJ6hE5DHF6Ug7TDvBRcpyFQNWOzlyux/pxkwO1306/TQwyjHxkIMsBE3UgEYNR143MgdpGZeGhbAmOYDMF1tlMqACQBm5gAw5cE1nYJULJBiTvK8zAT4yBjkVgsMFC7PTp+piSpO1cd1WO3eQj5NHgYAZ9rPLf4+BCHDGm9hgYrrzFp3j5xcwd0r4axjNgQLpDRYWeDFyktBco7jExMdMsYPDy6nHs+1bicaT4oF+pXHXBCrfku/j8JHzEtoXlQ4JoUPExNuoUuyGTCDTeBN7ZbjBQ1rvIUOGSer1RLXMFtIoBhHx6QwXLHwD3OaQCOcyEba6eMRAOaNHAAmNZUmJekpPKcHi4SRjU7K3wcy3MsJwUCOh+1OtvDZ4n4mA/bgC4QT/d2HDmceb+T46l28tY0xaeeK8EmgGLBs5sQmYaaGa+JLP7eC1JeZl52Q9jpxVmvXgrh3943ktUkApja4CQZMtTKKifB3zLm4bOccBl6g5yX1GdO3gmtLlOQ9UNO0yk5k6Z8YE2nY1DZAEONiRPMrYknG5lZFn7dACYcZDvGhD30IH/rQh5LnoBnlrOegNFzZYNzg2V0ZlDxEWGFsHgTWADAZeRkfHqZXbT1y9zy+9p9+GgO5Q3HCjIU3VAAsH51bYT77I3ZIKROt6YITA52cRVPbUHAzXUdmd9HFCJMszYpqGMvSQaAhARByRPAmzzHaA+DRNqwc9gg5g9ySqac8AKac9KdbSqkUpFr481i8XAaMEHhGB60coai6hjgDFn8eSrOSLaCPiilsgyaehR8yrHEXLh+BgGWyaDYJ4PF0Q9yWZZTQgKXvlg3JgHFnDl2MMp3suXbNjoFQuVq84AkX4U2/+UQA0EzedPgybZQmvFYLmLiGbA0YZf7MZJ1IQcp7sDTIAB8p4u9XPfNSAMB824LZmodNQnheNhhXrblSWW1bVcRmFxIIO5IMWQIQgYesMaHmtQzwosZD3qbIybUOUOAl+3iSw1p84T/8FK586IW57ZRUW6ysdLZauLPugSd7s5Is5kUu3EZBdsKHOWuACmDAHAAkBsBSNGBBlJ2IazIdi2Lkhfo5qAKj6bkl6uqQfI4KlLN4+i5lThBGrtku9K5lYEjaOHzsGK7fn27zE1UVpz8HxUo7bIh3vvOdeOc735k8B13olgGknXgKMrs4KM3sHABgmAioI0BoY0Ox+YLnpCCJFnrOvhyuZegB4mZpfzQDljVJiJfLyRnkzBcMWFobHxCC0Orme1iFUaVNasWUOyfK9kukMNM27IYsNSY5uyShM8i4BwAmRgdOmH0P8ga5ZQidQd5OddovR4UiBNVOk4/zWLxs0TEAeGYn1/wS4QQ+N2Bb0aKTEMI7XZE2KtDtTCBSkPHF1w85VnkLFBxtTDInGge+AHApAMy1KPq8LcB8xrtkZeyWKZUVWo54F5f66ayDYmTiC4ZigSyDwmqL9FsWA+b5wog1bdFVAGzAXZGSz7HSmGZekilIoYPLcuPXIvzYu/gbV12I/W95LhzTgNkSC3eQw6ZqAJa2IVKsNs3uAagMeVMBHBCBhwzwUchayI1lphUFF35sRVWQds7G0gizNzSWQUG1iD5LeF20cOeDwGAsAVgWg1aqQEowYPFxrN6lccgECFQATG5w4y3E4iL8OBvumsILTBkLq0KA6TuhZA3TGxK1OeOW8LUT88rsuxQw1Vs1IwVpieKguw8dwfP+6pvp90C2uUtr0QYIRhkQACwtmJcP4CIRfvYalVsgBSAwO8JTbhOJ8BsAJiNPhJ+WblBhUKJfrszUkzw+e6ISuzQnR7zNgzE8bsJJA2AQu5wuydOLxEX42YLdiZ/PoI3YrJErILxeBmjhW7fsz3QPF9VG2eDFN9po5TBgeQDMMUTqzOBBxJRNRRbNrVKyasHJZcDytHwQg7yD7EWTyH6a8WcQ/5o4PXTIOFdAD0RM5jQDtsIiE9KsicaGn+hbFw9lQ5HX/DdLL6JE+BPShkkY1gbp7zMPZoXTitm1TaqBQ5YVRRB4MAhPZcAMWZigGKysazBSdsutqRRknng7b1MGAESOaZ5TlIJgghFPaaQN6AVnLmdM33dyGfcNeLoHWOwzstgfIo1gkfb3Afzbn34kgBz9k/RkyqoghGFhQhw4OQCM5hXmQFRS5m1I0qpZEyE3t1kFTl6Kn1vq8TnXIAx1rcSGRrGpI48lfPHyRfjJDIdrUYz8iAGLqiCn/n5GUYtOQRIDQ+5kAlnlQp/JgJmG3pBkBSkopnjulZeIz8q4j6E21M0HYJ0cK4vcAikAvtlBp7Gh2JyhNWBpDJjSSqQBMEIiejVrkMpJIjP9pjxOctgf7gvBrpvR04yrFGKmBiyqtEnbMVN3Dj2Sd3y0S0uzoTBoZF/wrq/eNXt8mG6CmvgTZgcuz74HNPTAQAA6WxlnmaTQMDBMqVwT5y7+r55jnoWCAB/5g1wI2LMWjFntUSKt7PTQw7DQ+XvMVQoy9iPGIwCWk8a0EWACK7kLl/HUh27Hox+8W1QQ+un6JTvD+0mJ8FVKvr+anq7QKZOUSlCTkpjgNn3CZxkpFxURAMuuCk4T7Lp2UoQvnmNGSj7Mr+RUCzfPYYSPL69iEBjpsgIJ4AQQ5vjyrUdx8auvwaHl6J7ce3w5u/cdoO9jMFpNTUdTxVpkxNOveAiAbACnjJHzWO0xbecWpRh5mkoIINsl48znUAzAChiwSbqAXUfJAinBKEfvjyu/HvuhBGBiU5eeghyDERMMVPuAAZEmcQgHjBMNImfkDXozkCxEUJszP4z6SaaBD+FCn95bVZ2H2pBkBQ3Tex2reNZjBQDLYsA0i5cFhOVz6OVYiph58hCIfsWbzYaiAWAyosUqXYQ/4RaMNOaIRqkrK2uQKvuDTAsGBwHMXAZM+YClpiABQDbezbaRUFWMKU74gC7XzqyCjGmP0lKQkdtxRtpGsVc5DFhgddDKEbCLXdZsuwxAivC1WDWdwQp09d1UClJ+XggDI26D5ujY0npJJv6G2c0VsJOURS+edqBOLzNVIE5SpoKlYDeegvQChmUJwHpZO0XGYJEQHk9nwOZcC1c+9CJQcNCMDYGFIHUzoUT4asfeX11KPV6xR4YZT0ES/Rnxqt60UBVTWYsmJdIahgwz9VNmimA34QPGWzAIhxHks9p54m8AucJlSzKReSJ8xUR+6LsHAAA3xgobbPjSRyxjGpef8cM7D+JtU/0DAcWAZb/L6hqMDPYo0P5T2Z8xKQBgJpvkbsqUl1iWvx9NMUFNhG6tlT63+p44tywms0yBFIIJxlPjybXF1yMNwJIi/ET6P/T0eEpowPTvEPTh6muYxl/aH3GaAVPFRZMAfZmS90OO248mW52p3qzZKUgDy8xFN2M8AoLJzBTAA/o+tvkwo91fupVG9AcMBEY711w5t0AKQGh1cn3ENiIaAKYilwHzpPYpjfkhOnVlZQ1SlYLMAR9jo41WDvsD2bIjTTgNQOhuSthQZDXuhTOHFvEQ+Fll91GlTlrKQ7EOHYyxOkphTrQLfcZEB2gdW7bTcU4vSUo085LJgHnpXjXx+9FHCyTHdFGkILMXjNDqyEkiS/w9O1EpAPa8x+0GcXswCQPNqFxTE9WKT2GZSR+vSRBijUVteFIZsBiQTrOhAKAXXjtl0WJMVCCmsR5KhL8m/dQyWQPFKKe0YwpUKyMg29xYgaIs6wBEvedy2ynlpCC1AD0DhBLNgGUt3ILBymNTHfjZInzTAail06hqzYhXO6rjM2Q3CSbxMzcdmflx8aKp2J/08aDexcwUJISuM1Mbi2JNpTJCzdKxGcxDCAKk9MgFUFjgpMB8po8YNTCCk725BnSP3DijbBsUhEjNVxyASYlHYg4OJtqDK6EBm3ofVYZlGkBkvYvqT6yNfTk3i4bez3zbV/EXX/px9OeZ0PKlNSQX50FxwrNzGbBcCwgg4VX53g/8Az760Y8mfqwLczIMdQGxQc9z089qp6QilMfnWvyc4WgAmIzc1i8Z/coAwZ4o5sXOSiFq8JEzUdFO7k4RoYcJz2bAVNuRbBuKKIWYpznJtJGIifjTUpCKAetlMWDaQiJ7gHCri05OJWZexRQhBGPakdeQn4I07OmJKrqeNd7KNl1kLLdaCABCS4iGMwFYCutgUIJ9f/IMvOVXHlnIOnz023cCAG48OoZJk1WQg0mYsHBIpdpjnkNpInwAueJpXTGVspmgshfkGlNVwfkp+bju5nefvBcAsL3nJNINaRF5DhUxYDntlLg/U0jQsmOGuHpMZ+mnxDmkdWUAEHV2yAFgSouXOh4JkR5WgsVT0xNJHC9SyZkLii7dH6HrzAKUNB1c8gSVfip9XtK2LnmVzUYH7ZyNpclzqigBUG2Emv4u0RQ7kUQUFAIoA9C0huQqRqSVq2ND4GE8pakkhKBlGSkMmHgf45tM3xujH4h5PakBSzKyaoM+k6VQbGzGvLY2DkSHDTIS7ZEA7DuwHB2uGmFnjCeVgmyTCQykz81pG5pExN7FzvwWbNu2LfFjbU2TlYKE8DPr5bjpmwXykFCuLw0DtgkjVwOmq5Vmb5dB4xqw/HLvvIV7YrTRYjnpN6kzyARgbq/AhiLHCR/QE1VmL8W4BiyDAVMasNwUZBbFDKFjMwjHf/7Id1MtDPIqpgChNwGQCcCYZsCSgzx+PwZwc0THspVRzoKhtHhVGDAAWGjb4v0q0Kxcf6dgMiZywo+D4WHMM6hHhqlCfrVoTnLZVDFZpjFgAWOZu2XFgKk0qJ3xPpOU1i0vePxF2P+W52LOtQBKMSLtzB23aueUlc6msXcxt53S1IKhxtZC29JANkuXqXWhBamrTGPgMIBBuPZzy/qMiE0VzzLOgCkAl7mgxPwBe+4sAKNFrAU1MIqlvqaDFbWPgagKzpMV2DlO+gD0u5h1H6nsEJJ9vAKRNRkwAEO0YeeZKwdjTJg5M57cFACm3sf4I7vp3mMYaACWtKFQoYpKAMwUSul3cYrZTzBg3E10hkhUTzMGB35mGldVQQJCBJ8WBvNSzZmji1E2EmO8773vwXve857Ej1lGIUE8NIBKlVaEMBEWArCmF+QmjagKMi1tI3q2pTFgQoQvF5wsH7CgeOH2jA7aOf0kCRMTjZsinAZEy4xyNhT5bvqZvRSDKHWVBsBMQxg3tskEg+FsFWJYVG0EAFLv8ZUb78Y7r71z5scGzx/krd6C+CKDxVML9zRrEQcxfd7O9i1SWr6c58jsrhSw190p5gMwyiINmKiCjH429EJdiJBFtQde9BzzFn4gXTArGLB0vYhhiCrIlVD8LKslEynS7QAY0xzRrwZw6e8SlSaiLeLBD9JT6mm7ZZWC3LPYjlnLZAEwxYBlMCd2PnOjBOwezHQRPgA4c2jzYSIFGX/ews/NzF5QTAc+N9Alw1QAVsiAQbA/WexR6OWnggEgMLpo5wAwC36uLjRKg2YAMObnVt/B6oCBZFffyeeQ+RxRzICdXFnDmJt4jDTmVtGyDIz9KRsKyV7FMy5cbvCBZHVkXCKg9LXxz4guQs5rzmwvSCBiwDqxzhDx9yhkHA4C8IzshGsael7pZQEw7qeaM+swXTBiokuG+NAH3z8DwLiem7OfA5MAKtUHTFk95c3NVrexoVBBCHk2IeQ2QsgdhJBXb9R5qNAS/AwN2LRHiwpKCRgoBtzJ8QFTVH02+Cii6mk4yRXhG7LtSF4vR7Hwk9wUZJFpY1YrIkN2vBe/OiuC9zUAyxHcql0SGaYuSiabTRvF47KLReuTYJTeAkdV2kwDsLiOakha2caTupo1h8VzeqCEI8zwEitkHex83Y3JxHP4iUvOxwsef1Hi3AdemGjDEzA+A8J8uWhOuJVtPqxaKqWwP1HFVLoGjDGOU4GYBN0sBqwEGzqmUUulGU1gwfGURBquLDCeVs2q0nQXbmmjL3VsLT5MBbKEefC5AZolgFc6uoyF259EqeA8EX2bi16QasGmCQ2YmJcyrWdiPfjSUpAm83JFy4AAwlnsT8REZj9H3+pka/lki7a89yBiErPHQ66OjVIBIrP8p1J6s07HxOjkmskG3giW08KzH3F+4vuuRfHR7x3EV+4ZCYNqxgQgQ3KdsbmvWbykBiz6ei3GgI2nGTDVXSPDB2x1HGgAp96VOJPqByEc4mf2YXQtQzPrWVYUostJznMkROtj87SpmUUtALgkGfw0AKWyTDkbCmZ3hM45yO7scKZjQwAYIcQA8JcAngPgMgC/RQi5bCPORQXP8QFT4CfTcRpiws/UcJVIXflGJ3unyEJQLirX3AzhNHF6MAgH97J1aAq8pDNg+VR/PAWZ6oRPIhuIbsp1BMpxOmeAUe31Mk4FmkWD/IqHiP5iR48fT/25orlNJ5sBG5FWdlN0peXLoblV1ZXq2zYdJstPo+qUScaCY3Axefx/z300nvPInYl3cjgJEMJAYLjokhF+cO8yHvTaT+Nbd53UvxNf+AsBGB/MgJ8gZHDgpWr5DEqw/+QQ3z7kyePT30VaZB0AkU7uyl6Qs75HikFLv4/xwpgsE1ErpS3WjjkX/+N5j8LzH39RQkuXpjkx5JyQ2gIIACjFhGazRxoIZ3W3AACnhxYfwosxYHERmNKA5e3o1cKbNp6MgrJ9ABjRTib7o1vg5LDayhcvtbCGBTDAM7VHACIRfSYDVlBIAGCcw2DpOSFH/M3sfABmsvQOH+qef+1eeZ+8PsaSvYrfD0saIwNI2lAkGLB2JgNGmSgCoEbyGSdF+KIXo/qz8TlPA+ksBsyKNtdZ/VlFn9/85xBKDVfq65qhz40Ht3voYpyuc5ZzQpizGVA9MTONhTcgNooBuxrAHZzzuzjnHoB/BPCLG3QuAKAbdKbNE5TltAGS0eetbA+roHjH78mJKu94oQHLF05narjCCUJigxKkGzdqBiyf/clq5q2c8IH0XZJacPJy/EZLCjUxTlSkqTB5/iC/YPsWhJxgMlhO/bmabKd3inEQMzbamcUUXDOZeQuGGOQsQ4dmFPTDVEA4q2rLYEnwMp2CBIQVxhwZ48ZDggn86PcO6t9RaSMPZrbZa6x6brrkOwh8AfRT7oEC5ioln8XoEuYhBE31c1MxotGCMw0wSEEKksQKY7IsRbL83H7tyj142Pk99KX7eCejFVCR8SQgWO0sDZnakHg8Q4QPaADmh0wv2EkRvli480TFyg8t7RrMAhNUQNhIZHk3qfRdHqvNrB4cEmjgn/iZsh7IyQxENhLp52ByL99KAwJEZm2OlY7Nctq555DGBqswMio5lWa4H/MnHPsqBRn9nsV9nYLMEuGrql5A+HrFx0TWu5gQ4cuUvBLRx1+5IibTiTFgvQwNlVXQZg6I5BnpDFhBUQuiNm2p3n5qbs55n7l8l/K8+c50bBQAuwDAgdi/D8rvJYIQ8mJCyPWEkOuPZ7AapyvyNGAkr2mujD5aaGcxYCUW7lAaeOYdL4TTGdYBSsOVhe6DCXya0Yg7dny2lUbEnKQBOFO2AgKQ6lytPbhydsumAmBkqHupJX6e4cCuYr5to49WdjNtf4wJt2a6CSS8tIxOph9bmaovBaCyGbB8r5o8CwhALDjiCwXAkiJ8INpp7uiJ37nnZPRZqhJ0Aiu7X2Wsd9x0UUeoFtK0FKR8tTgo+txFi6czH1Q5sGf6J8gUpBwPMxO2ZtAyRPgkMtWlGRsK0Xw4/fjFjo2P/9EzAWQ3uDdYpNvJCt8QHlhp9yCuxcv0fHN6aLFhQnSsnzdjotckzz+HNbTQy+iJWWQBASgfr6xq1OLK5tAWlcl+iixgMla+fMUbmixZgJHS03M68sxg1abKcnKYebeHFkYYeem6TiuDmR/J8Rhvpj1SKcjYOmNxoTEGAIvGNWDTKcgxCMTxx9cina3K0EwHSTBgSRF9nLnVQDgjO+FaRkJbOk7Rt1oZ1jTx4FYXXQzzU5B5AEx3a0mxOSqRZYoqkxsAVio45+/inF/JOb9y+/bt9+/fyvkZZV62X4+MgVxwUkOV3eelIK0u2mSCMMh+ubwM93IAetHME9GHJL/iCshe+OPtMtKd8CPWYY7MTvhKhJ9peIgIgHUwTr1Ou6BkfaFtYQ1tsAz9lRC7zvZAnAZgFvejNFcsWIaTfjxUGjWrEjNrstZR4FtkMqlfMGYB2EAuEKHdTeg97j4RLT5qt+vlacBMGwF1MUeGM5rCUHtwpacg9blAVF2lsTNpfRin44LzdmgGbBaA5e/YKYnag2VZilgFdiK7d2wFI4Y0c50FLzQsTn35ZhftFBALREzkBFZ29wmnByccSBG+qoLUHwAA+RWAAOzOIubIMFW4nJaGnQ4vx5+QpTRVn/kdS7rxD2c3JJOx+NzM9jMAYDrCZT6LEeYFmkoUADDpe2gVzEs9jHBsLT39ZmUA2cFEjMc4A+anVEGaMQYsvrmNM2ArvAtKOLaZ4hy+edcJ/bOsNGzShkK1KJOpvjQAlmVDYVIMYtmNVADGs530dUgG63V/8T58+tOfTvyIBBMEnMLKAfPUnYNFQq0nToQukMqrsi+wWtqA2CgAdgjAnti/d8vvbVjoXWrKomTkiPBVKKO7tFAvOMtZuEOZn/bT2JsEA5bPYGWXvU8QkBwdm6wWyvZumoBT0S4jtQqSEqxA7Hbn0Z8FYCVy/HZHMWDjGXAQMiHYzRvkXccUE4WXwYDJ5zgNwOIgRj2HNAPNsETZPW3lA7AirxrY4jlkpUGtKQZs2gkfkH45sYX/RKwptmLxPJi53neeNYcF9GcAmLLy4CnvMk1UkwrfobSScVqi+u68bduw3ZKVTVMgzijQkFFCYv3zUu4jY7ByWq8AAAgRveOQbm5cWH0H5bydzpwEfsQoZwOwOdh8AhZ4ejzoexxEzzEvrrh0L7Ybg9QUZBZwiIdnZNtI8CL3cgBMzktprLQv07B5rDggbSAy5iWrqKoYAoBlFjgFqjAnW4TvdBfgEB/Hl1PGNOeC/UlZ+PsTsZlei3XoUO9SfOwpict0xOUmy1zMrY/fSbHQtvD1OyJdp5FhxaHItKWhj1A15JZu9vF5Q0kzSAb4adlGTBM5xDjlfS2amwHlVTkGN22028mUryp0yyQIABhujr62hNOAYsAaDRjwXQCXEEL2EkJsAL8J4BMbdC4AYj5gKT8TAyTDQV7GGlpoFwCwXI8TUwyQ1PRZ7OXKFP2qcu0cBswndjaIpDS/bUgw0fn1LB+wJS7OYYHMTvjahT5nsnXaCwDEIJ9edL1AtcvIoagJwYi2c9KwY+2fNX3u+jwVAEup5FQ7/lzjSOncnSn+hg9m5CzchGAEN3PBMSEZsJQUpApuSzPYlH6ULLbw57G+nr2ABTILpNcG4v1wUqrGpjsKdLIE7CWE03B6Mu3EZ0S7hOUDMEIEIw1kiLe1L19+yiQ0O7oX4+w1TArZJ2aLsvdhCgBjMT+2LOPhyA4k6mqg77DWZBbcx9YW9Hg/FURaOe1nVPhGO7M4SKUg8xgsxTqkVSZ7E7Upyz+HIcnWZRrcL2TAhMl1vr42b1PV6s4DAE6dSultygJQ8NQ5YSAB2A6VvZmsReMp9kpR5qcC6TgDtgRxH7fSAfZu6+Do6jh2fL4G7NjqGEZLXENXpyCj3+OqQt1KB6GuZWgGrZeyodDdMfK0fJApRDLCp/7xPXjHO96R/Jls0ZYLwNqqwX3KGhkWp8NhKZ30OQ7AOOcBgD8E8DkAtwD4MOf8RxtxLiq0BiyFFSjjlzOQzUrTBIpM0qummb1bZTKFGKZZKITFTvpKrGpkphBFuXYeiMxtGxJ6epJJZ8AoViUDtkjWZpkTZQHhZF+D2+6BcYIOmRVqegGTpdLFO3YrA4QS6ec2k4KM0/GmctOf/QxWwvfIlLs0kpOCLFr0BjkLjsWSFYBpkj7u9BKeP/FQIHICK70aSYbvCAA2zc6cXBbv53yvO3NMHAzOzS+iQ8apDFhh2xIAcHqgYHDhzdhAFPX/o7GK3NSikhKtwQAgsLqZbaXKgEguW2uNUgCWYsAm3MIky0ZCAbBwEDnhawYseo650VpEG6NIrxULuwRr4ZtdUfUapnW3UBvLPOG0LEpJWTQ9WbE9bSA6HXk+XCb34JdIo2YyYCU86Wy58E+GKXOzuq8p91Gl3y/eJewp+GQtZsQavdMG91K1fPFsR5/KzS3WQAmZYdDyUpAn+hPYWl87SvwMiJmgZoCXR14wj3//tIfCl70Yp1OQXig6hBTNzbQ1hy5G+NaXrsGHP/zhxM9IKDbHVpZGGYDpChCZmkIsYfUE3Ve00YCBc/5pzvmlnPMHc87/bKPOQ0UeA1ZmwdA7/pRm1syTL1dOClNpJcJxystRopVRkYGnYsDydGwi3ZBN1asFK9WGghKEMLDC25jHYGbRUgu/mTNZtxxTVvuMMb0mTcJQ+E/l7XAgyt6z9FNE99OcKteOPRaWo+FiJXbLaqeZxcJZ8MELmJcRchYc+CLlIp9Bmh5PlGuni8cTDFhOCjJ0FlJTkCdXxXnN9zozxyhg/ts/cSHM1hy6GKdqj8wSaaO4i/s0GKcs/znEu1OkthIK820s9K9J0XCat57B/cJNGZwuOhkpyOg5mNi7dfZeiuOlHQgbxgBY8hqUeDsz2sIc1PFnAVCZyrXAyu4uoVpC5aUg1TWwlHnNL8uA0eyNoVVgzgyIjWULo9QSdxKORUFI3sIvx3SqtlTNzSn34LEXLgAAtsu2O95wVc+LCQ2Y1BhPR5wBGxriHLbQgfTbi34vSwcXATAPTjfJgCXmDc1kZjNgf/SMS8HkvDJtgzHxQ9hFfm4AzNYCumQMlsKKq44GqRX6MoxWTqV/gTUNEGvsntPr90zHphbhn8l46GgfHktuTxUmi8m2AIDxlqhKmqQ0MA4mhRoyxYCl57fzc/QACquFEE7gF+TYfbODVmYK0tMAMCsFCQBLvCcYsGkAluHBlbgEk2rjyGmLBD/kol1GwSAPrG4meCGBl+q7FK8M5Y7cZY1nd7tadJyTRrUcV3jypLFwYQATDKzgGoaklVn6b005TqelpKkrGLBJmLLw66bqJn7q0uzCltBdxCKZTV0trYrJr+XOlu2rd8AxDYRWF3NkkGqaaBQ0zRUfInbsaS2Vpq04poMQIISBPnfhBGmTtdJk5j8H5bydxYCVYfG6GGWkIMVzeP6TH4rfuGrPzM/V8YBIyUcO5uIe81hVcm60BABz/eWZH1kI8j3tAPim2pBkz0t5fRTVc+Qp0opgkm6MPB1D2kErzNNUFtuBGGBAysIrLBzyQawlGbDUe5BTEPKe370aX/wPPwlXalv94YoG8/FX2owZscYjrgEbG+IzFkkfhExryAIEqSnI6GunuwWAKJACknhT92HMK4aAWKPmyGDGCHbiTWAQnuvxCACG3AyAzRaaZengEiHfpdQqRsWA5Z1DUaHaBkQDwGQ8f/mdeKmZLkMz2QQByX85lQA9SPGgUq0mprVH8VAALG2iilKQOS+X1QYDzWR/EAiaOtOGAso0cVZ/pc5BCXazfMAAYBkdLKQxYEFxtREhBKu8jTkymHVwlxqwvFQBADC7l+M/JdKw06AlcUsUzT1enjme6yrInHSFBJFmCuOgJ+si80vSzrTCmK5cSwPD3bktsBCApKSdlHnm23/7J/B7sgF2WnB3EfPoz7RUWl6T55WyGVDgwLEoAnsePaRX35ncLzQAhdTSzWGI6Q0zLWCw1HmsIH3hVum4IiaSOXOYk82wp8MsAcCo24NNQoxGaZsy8S5dvmdbjq5TAjAy22JMCdjLaMAAwA2m3kcWwiSsEIAFthwPo+WZn/FwjJATGGb2ORC16E5mj1eNsPME8AAwpD20WPqiaXO/EMxPFIhM2VTRcAKv4PgIgGUzYGlzwpxr4SE7eui0XAy5A3+4nCrCt7jQP023i4oz9RNTyDPmeB+UkASAM1h6G6D4e9WekwAM6l2MfqYqQfNc6AGAOQuYx2AmBTkZqWKMgjHdWpAflALASvjqqbnZ9Gafo9Yj5jBgpuWITi5NCnLzRZ/0ME8GsylIzksJPVdklQobLM38jPtjeNyEZWazT9xdEP9PWfihc/T5rR7GtAUnp4+hV2ClEUrNSlqZMQIvSkFmVEECwDLvYYGszQjAFfNiF6QbVtDBPAazPmJhCIcEhQxY6MyjjXGqjQRlfupOMU7HEzlJ8JQFRy2aeaJjy6BY4R2Yqbu04hQmAIxoC25GX1ELfiLlkpaCVAtG3FdOAVo1Ue1YnMte+AHw1iJsEuJvvnwjvn9v9E6vSBF+GvhRH+eYFKEzJz20Zt+lMi1wFHMzTwYzKUhTpVwyzl87gGcAsKCElg+QCw4ZpKYgTV6sC6VywfCHKSDQL8MeRWnY6YU7qmYtuI9tsfC2wyQA49o3qaCS05bgIwW8IPAK5xTTdjHmFkjaeNKyhPzn4Flz6LCaVcXyeACpmyoSeqlzQuJvyM0Aao7pOdfS/oQKzEdV9xwWAuzetoAbX/+sxHFxKx7TsrCKNub4Kiid1ZCFJKXZeuyxzM8vIgTFPBHrQ9x7Thnq5rVjAgC0xHh4zzf2419vPaa/PZHjySgYTwpAETY7J5TpaKDmhDwAltVOCYg6ZBjnugh/M0afdjGP2dYrqsolb5D/4U8/BBfs3AkACEcpAKxECpLnMC96kBdMVHmu1cqCIU+Ez6R/VJpoOM6AZfmAAREDNiMA9z0EnMK28ifLuYWtmCOzZn1KL5LbzBv5k60RprMW8XtCJQALUoA0fJUKzgdgq+jATmPAcvQi8RiRdqrmRVQbJc1oUzGIkxTcApEgWBtPFqR9iGRObrpjP37lHd/Q3/ck85IqOpbP3DENMHselHCwlIW7zKIJuSFJA+NGgZmtYsD6pIN2CnPCSvQwBADuzgk9YwqILLMpU752wXB59rODEum3FAZMDQvlhVSoAZOLVmuKAdOmwgXpu0Cm5NPmNSKZ/TxW3TEpVtABSWHAtPi7YF4L7HlRiRnOMicWSpjJKgYsBQQW9mYFNBtrpACw0C+el7quiTXeAhut6ndZv9E5lipxDRgHsMy76LJZET5hfiqrHtfqbu06GBk9zMm+nHF/vvFYjOluJ6cbAACjs4h5DPC1H5/Av33Pd/X31ZxQxKCpMf3K//hHuPbaa5PnWsIbUAE4O4XBCnUlZ/a7YFCRYcl0CtiAaACYjAHppjNgcoDkTfivetZD8cRHPAgAwNKo+hIAjFgd+NxInSQ0A1bwgo+NLlo5DcH7gYEtnewXlNk99LIcn4MIgKXNt+aUBmwmBSm1FtMViNPx4At3pzNgOv1XtNvNTpnQjNRXnAlquQ7WeAvBcHbBUWnUrGohQA7yDACmJuuihX9EO6nPMeSiEXZc85IKqDVzEjNgVfcz8IXhoV3g2dMRAGyBJCcrqqrhUgCY2t1bBkEoF26eAj6sEj0IVbpiLoUBozzIBXC6JRLppQIwNVkXMWBwF2AShjBFgG6WEH9bnQUAAEupbFYC9jxfPK2Dw1ALn5WDujbULakB67DkOYQy7VS0GWCSAWMpzxGyqCVPV2oZFMu8C2OSoqkscw8AcCeDhZN+buthwGhRb1ZAL/xGir+g7nGbcx+7jok1tBKbEf1O56TT470gg5BjGWJDQQhBPCtOQi9VWxufpzuOibEhNFxA0ltPGeKmWcvEw+pswQKZnZe8SbGLPf7/9v483rasrO+Fv2O2a+3unFM9UAUUAoUQoVRAxQYiRBBBJfZJjARNFOVGvWgiiVFvbN/c+OqrXowm8QVNcpXg1YgRNWiMfRQVFQQiINJDUVWn2c2a7bh/jGaOtfbae40x1q6z9zln/j6f+tQ+a++51lxzjjnGM37P7/k9YJ/pfMnc6KWpTFL22KBcTKczjKXjAmEzNy/7/NPCGIBp7IotzrN7uAxSPyCrGo1SngeWp64UVZ8dqwHL9U4xWUb163NYRfFW2Tab8igPrJoHKsEjbzpml1OeY1scMKsOp+/oGkcDdvh7mEDgMps69bSwW20rdQ1WBGBicp4dsXdIeG2cw4+tuGLQrMglO/akb+iXUPUGj71ti2mRcpkN+iUBmNFwrWLhrrC1dJJoVzS9NdhLttmQe7BA1Xe9VF5ozoJhgo2tUn2vpz7qgl0wzjmTpbmeUgfCx/U1BUi1J5sR7drPk0cHYK1Oa+RpgpwYz56Lh/4uk6vtRMxu+Ty7cxVjUksCjhNem+G5n26xteR5MMEHK9JvJh29bOHOZLtUd+Oi2FLBz7I5wegJ8+NSkMUmvcjm06AmjtYi/lUCcootehKmC7YmXeunR+wNA7bkeTDeTcex6nmq5rVlAZid11YwYEfKM3RXiFUs3rGbsr5dvfAXW7Qk5EsCMMNEHrc53p5kysneOX87pvWGJlnC3OSpsGnErpdclptMul0SMaQw+14i+oZiSfDjbiyzRDBLtzmnNWCuPUw989uQpBuqq0LC/ObaBGDZiuyGeabf8Mu/zL/+1/96/r1lu1qWgMpUHdIzstpMFtQadUmOAdiZxJVkm4loSE2Ju0HrGYCZyXrpTlG3Mjpm0cu0dmgZVT/0yfIIwI5gwGRXcalJuOu4AEx/h3pZ+q2ruWjWrSWjxjzsl+UGiZCHzfL0ZL0yAJueV9YDCxV8ZtFc5ZptArBlAVTa10cu3D//dZ/Kf/7qT2GjSLksN49kLTopSPMV/feSLSZLqu/sZL0iANtPlpvBdiYF6TBoZoKe5Cn/+Ws+hX//4qcN+imcAKwbdtwNGWV6RE9RjUyLp3eYH0+2AnFJ8NJaBixB6g3JMu1QhkcAlhW06QbnxDwbajoi9MdM1iZFvpdss70sADO75RULv9DXURwRRB53DgCFrjxbKivQz/SxTKQQ1MU5ldI3KUhzuGXAMh5z6xE2Fvo9DtKtQ0xgV5s07IpCgnKLViZ0S+Y146t3nHdTkal5LV2i27FVlCvuQ6I3A/XughGqyU6sstIojpEl9PXquV0I9tikWLap8gnAypxLbM5pl+z+sjPz2uFrIISwQvy2l1xhStleUTYU+g3u36spaCmXMWBOAJYkglm2bTdUraMBq2u/lLyZV1xmHdxiCj8N2J/++Vv5xV/8xflz7Ven9EHNjcvmViMrSI+ZWzPLgI0i/DOHK0JNYodEu74M2DG7ZbqaRqbHpiBzPTiSpTtFtUtaxYA1+Q7bLGHApERoF/jjAjCz4DSLEx2KvXn7fToVeox4+zLq/Q/t+k0K8phroM5BaYfSBQPNzvadWzHZ6nTFsh17KtsjF8177zrPhc2CjSJVFa2zJVo+/R2OEx2DSmeX3e4h3yHDWqwKwA5MALZwDTspKcQ8A2Y+IRHwtEffxLlpbidKN11gJ1zNxh5XEAKQH8mAaWbzmBRklgqnmnSZBmx1MQVAUygNlpuCNKaPx7EeZjOwn2yqgowF7VDvUTEFkOggdBkr7WN/YIJYseyZbitmMifPjg+E23xHMWCLInz9PHzds5/AL7zs0459j1m6pRhVB50nG3thq+QyGzS7SzZlvbJPSI/ZWBZaE5kvE07reW0Vc5LqdPjBpfvnf9H5MWBNrioIlzFgqUcgDYp5WWZpYtspHRN8bE0yLsqtOS82w2BZC4gjxqKxouilYsDy9grC8QH74KUDclom08NMqjtNZYmgchgwd1NjeyuuCMatLnMhDdk0ZjOx4pnOpzRkJCzXVK56ngAO0m02lhRk9J3ZHB8jTdAasGWeeKeFMQDTuIJa9A7dXBuArdB75DlX5HT5ZGvTPseY/WkG7DiqfpVfTpOrsvlDhQR6oqplzl0Xjk55mJLxdv9wANY1lU13HJdyuKyrQRcZMNE11PKwB9ehc9AL9yJNPHhwraiCLM6rv18agK02ntwoMi7Jo1LB1cqUC8BeskVKd8i8sm38Upj7qSmbvzj/8Z3SgLnf4VCTZrCbgTkGzEy4/eqxCJBvHcWAzbdCcjGkIAUYQ9qF72BaSq0KQkGN58UqyKaV5HTH3kdzLfaT5fYDZiytDsDOq/8vPpO6cm3VnGDuw7Jn2pgCr7oPbXle91Y11XPqdRNA3XbTjk0/H4Uq3T6UirWmwiuuwS1bJZflJs0SVlxZB6TkxzwPRaY0YMWy/qxWA3a89qjQWrrZlYUAzNqJrAiE05QrTHn/hz546HepRz9M0MzLkopao+s8rpIzTQT76RbT7gqC+UD6wStqg1NMls8JRojfdj2X2SCrL5M4PmAfuDgjp2VjSQDmFkuliaByGDDXWqW1AZjfeD63MCc09WqTbQCEYDfZJpWHiylS2Syt5FzEQbq9VB8r29WbY9OvuGivLDXlPQ2MAZjGrp6sNxZ3OZ67LLXT21jKYAkP9idLxSGa2sJ6aK3SP+2wI/ap6oW2Id2Qrrht5+iHJNM7Tblksu2ayvYr82HAxCHBbE0rsmOtD2Bg4RZF7F3rxx4Zwe6yYojjGDCDnUmu0qhHMJHqIT/+sTkygPJlwFLNgC1cQyXCb5aOxbl7kk+pKDjnCOht1ZNeNFexeMXGjvIdWmDAMrk6BZklCULvlhc3JAd1R0632jMIlU4+L3bndut1pxtpH6cBMwzYEffBBvMrUl+GwToUQOk5oU9W669akqXVc3QNlQcj3JU7c4UQ5kpYXz0PJrHKtthaYMCGStBVAVjBJTaXPk9CB/MrNWBSd6dYrGLU6exVDFi5fTOwRBphrDRWBFCJUNqfP3jruw79LpPN6uwGeuFfGoD5eWjN0h0SerbQ1af6Rr7zQ+o7Pfzmc0uPMwGY0YCl3UzNAfr4B/ZqChomSwI4sRiA5csZsLY5uhBg/mTOA6owx5WSGGY/P8Zk22BPbJHIJdY0nkzkLNthc0lhjWxmitk/5nlKhFDXUDbQHNEb9CpjDMA0rmgj1Y1+UbtkdlkrGLA04bJcnkIUmqo/Lu2TJ8Y/6vBOUXbGQ2tF65TiiJ5lerKuyZkco8HKTQC2zEpD6z2AY7VshgFbvA7Kb2f1DocjGTCzW14hFM0LrsgpLPkOuWxW+h5tTzIdCC/fsa+y8gA4SJaXvXceTdkBZuny4/veMGDD8XfsTPjKT7ubV7/k6XN/u6dtVexnO1WQDYfNaBeRZapya2dB75Eem4IcGLBksk0nBX/+jr/mq149lKzvNy057cqKXlAWCDvszRmxqhTk8iDU4MKG+l2TL7+OxgNrVRBoUl+Hqt8MK77KzV8I9sQmRbOcTfUphujKC3P3cUhdae3QqsozoM622WIxBamvwYpF9+bNkstyY6kOTnSN9gE7Zk7RG0vgcDraBJErnoeJDsAOWcOYzfGK7/BXH92z/oKLSGW7ck4AncZdVpmsn+lVOrZaz81mU2Qex3d9SGUb7rxleQBm+kG2vbSb2w05sMJ7s4ZCdEttZdxpKk0ETb5NKRpKastWSynpm4pOpMe2YwLmtKU7k+GamWuwyuMRlLxiI5NMFxi7VLarq1GBOttZquuUZnN8nM46ccfixZWfdTUwBmAal4ViHaaLvePsQ378Q3pctY+pFjouOi90FWRWX2HR+rtrayqZMymOD2A6LXxudhe1Emqia8jmvGUOncP2caLhoWXHMh8wg0t6kkgWFi1fkaWhuRcZMJMyWaWDsxP+kgAsY7lfjoudaa537PuHGxB3ldbyrQjAjmBeWrtbXhGAHeHc3eoATC60IvoXL3gij799e+5vd5PtOeakdVKQPtVGoILpHTEfxGU2AFuWghwYsDxLucwmVy7ezxveOpg27lcNuehWV0yhGLDDKcieQrTHLrr/6gufwis++wk8+hGPAA6zoT6CXYB84xy9FIdY6R/+b29R7+OxYOyJLZXyWIAJXlYF8315bk7LZ/07TfrO4zo2+TbbC9IEq4NbMRbPTXMus7mUxUt6tSk7btEzInzg0DMp+oZGpmQrdHDb29tUMqdffKY7vxSkRI3lm9PDHomZbFZursEwL8vE337yEFOJaYJAcy/ee58aW5vT5drcco4BU3+zKYcq8YPqaF8/1zBbBWDqHHYcm5+DpvPWwdkUpNibc+03wXzhwYDtp1v8///+Y3j9618/93om65X3EaAutlVzeGPpoyE9TIGNDxiw3Fj4FDAGYBqX5SoN2IoUZCaOZLDURHN8uuExt27SFjtKI7Aw2XW1Sv+VKyoITen/oXZIpnHvivco9E5zUbcDasEwAdhxDVMPEjXZLrIGXoaHMJjtLSxavqaNmWYSDwWRfUdKv3K3vFVmR+/Y9TVYtWhWRzAvRrezatGsjkph9pJcrO7fB0qzcn4ZA9b5iV1BpZNdBqztewrRIBGQHF40DQOWpULtNuXmoRTm7MDTNRvoinPKE84NwEwK8pj7eNNmwVc/82OsF9mipnHwc1sxllJlSbJoP/CTv/0OAPb71dPnUVVbom9oWZ2Sl5Pzc6X/xgdssKbx09LtLHjrDc7hK6ogE0GdbZMvYfFEv/p5yJOEi0exDnpjuiodvj3JFPuzpLAHQB7TCgng//jcJ3GJTW5KD6edMvwYsCbbZlPuHdIOmXlplbGx8SIzmyLzLnV1vAB+MseAqeu42e/Z0zAeXstkDYsi/Lu0WfiOGNpr7Vat1jP6zM3nAbg1O5jTnJp5bVUQCmpzuiyFmMl2JRsL2CDy8FhSm+Pj2FghhL2GS/02TwFjAKaxJzS9e0QV5Kr8+NCC5nAAlvTNSgYsTxMec9edwGEBuRLAp8eyV4B9QA5VAOrv0Iqc7JhzmE6m7MlyKYuXysZqwJa2KgL++zc9i9e9/PkAh65D0vuJLM13WPTRGkwbVzBgesd9SIN2jIGoizQRQwPihYfUpJJXacCqbHnZe+9Zdl+nG3QkhzVg2gfMZ8HYS7bnfcD04pt0td9uF8OAOQFYJynoVAC3JHBwbShyrYlc3PEfaM+hlaaNQFeeY1NUQycIoGpVALZSr8LgYbX4PJngY1X6LrGi3fmxeNNEfZeP7B1uUbSIg2y5dijxTckbQ1p7HfXr1r7ALwDbFBVNPfj7+ZgKG7TFDpPFzADDvHZcAJUkgj2xvKrXBHCrgtAyS7WVxXJZglhhn3DTZsHWuZvZWthct7qi1mcs1fmO+tsF7dAQgK2qUJ9nwAYj1uO1eGbO//x7H+4wYLv2+Nns6OPd65oIweMedac9B8uA1Z1i1T2uAfkEsglPvrmft4bx1ZChArBX/sYH+M7v/M7hRSlVIOypC1VvdHHudSvCX5Gd2DuiMOe0MAZgGj/wZZ/IZblxODo3PQVXpSt0CnLZTjHRE82q1JUxblysOOp1BeIqBmwIwC7Ov240Lyt2OSp9t0VWXzz0u0wODNht28sXrrtv2eS2cxtcltNDvRCT3qP9DECxRYegXPRq8dSL5HrRPKRZ6fx2/KAWLODQQ+pjPAlHtz6xve9WGR4mCbtic6kNRU7nNdHtJ9sLInwVLAgf3yMNxYA5Vhadrv47Qvv0xU9TE/zjbttSRSVOAGdsFGa6bH9V/z8YUurufWjsorn6O8gjnodKsw47K1qvAFxm61DZ+iPPqefgA7urA7A6PaKRdOfhwA5zPTHBCcDamlqmZCv83ADaQo3HxrkO0s5rfoFwIQ+nfRIPBgyOLoZIHFnDcTDtjBaZyCt76ppsbay+jwfGpHrB0kQF86vHYntET0wTyK5MQerqbJNONvGLWLExNHP+//lFT+EnvuY5AGz2QwBW264Ox7ciypLEGgs/4xGZlQvs150uavEYiwDTC2z2u3NGrsZKw+eZnGXb/Na79vm1X/u14UXd7s+nMOfIrgY6pb+qqnhXmBTkxWP/7mphDMA0Hn/7Nnvp9tEMmJcIf4O8OzikHUp64710/OXu9YLT7s2nTLq2ppb5SgbMlM0fTdWvMJ4UgissKRnX6buH37zDu7/vc7h1+/iS6ytsHApEV/XvG75EwhU2KRfug7Ts0fET3ZF2Hp4MGECnF+5FLzDh6QPWZZuawbo4/7ph8VZoJVTT2M3Dk73RgHl8h710Z6kIP+n9qr4ArrAxz4D1vXbiX378iz7+Tt79fZ/DbTsTsmSeAZvVKlipZkaz4hOAqd2umAvAVBDok65IiymVzJELprom7XPOIwC7wuYh/6eHbann0Cd4qPMdtpYEYE1TebFPhgE7jxFvz9uJrAp+QLUYA+iceUF6VhWDEqCrHw4/0z5pVGsHsqjh6v1YQKMjWyzMubirxta5rWOMaDVm2WEGS1mi+AXz3RFmrl1T0UvBtPTrKGDuo9XjWVuXoxkwIdT6cu6cCsbLfmZlwvXs6N6scyL8VNgN+k3pgW3GvV+3FKLxugbqhM6rAMzRKZsg1GdurdJtBMx3+TBsrk8grAOwQ4VinunskQE7w9gTW0dqwFbRo4VmwIBDN9cyYCtSV90xKZN6hYAehgDskAmqcf72eMiuJFuHHZ+tFcfqB0wIwT5T0naeqk9ks7psX2OXTcp2ftGypo0rKkEHEf5FvuGn/2T4xTFNbxdhG6MvS0HK1YtenmXsLWGwpGHAVjl/J4IrYuuwiL/rKcXqQgJQVP+WmJGhRPOtE4D57nb3ZcmUIf3X9tJbL5JbBkwtkqbBu2Gf8nL1fTCLVup0h6i1CN/nPprClsWFv64rKplx/pi+qAZXxOah1ieJXjS/6ln3rDy+yXUF4qJ2qJl5sYDJhiqMsQyY+YXd8a8OwGSmKs66angmQ1KQRxWVJB4NycEpKll4HpK+ofE43jiYLzKRl3UAdn5ndQC2TFdZaU86H+alO6LVXFvPaMg4t7FC2lBuMJO5fR7McEj644OXSZ4MRU+5uo+FnNkAbnCxP54BS8Vgjrwlr1gGyzBgPs8TANPzTPsrC3pC8x08LFEMg9U7liSeVckAjekxuzg3m83xCgbsQIwasDOL3eRwyw4TfIgVi5ZJuQDzN7fvSOhVy44Vk6VJmSw2vjX5beOKfBTyyQ6tTOAQ++O/Q9lLtpgsplGt347fLqmiJO3m0xXpigbKLq6Iw6aH5iFfZfantHhb5LLi9W969/CLgIecidppLi44wqaSV9yHNGF3SQBl7A9WsT+p0LYoixowz4pcGBbNRd+fxLPsHuCAgin63kupBPDCw4AUxURedkr/TQDWHFO1tQgTCLuWJkaE78OAmSBw8T40lVo0tyerr8Ou2D5yLH7C3bevPL4tzx1iXpquR7aNFwuYmK4GCxows+D4MGBm4e6qgc0crDhWn8MsOYIB8wzAkqykSqaHAzjNoK2CEIJdcZiJvLyrvs+F7a2V7zHoMofvUAfpCdXxi/KQtq6oyOaqApdhkqvNwHmUh5Y1ULbM/BEi/DwdAil9H0tZ2RRmc4yJqktMpskQgG3K/bkUZOEpa1AndJ6N7vKckesQgHlUMS4JwKylildltGkOf5gBa3zW2DTXY3FkwM4cdsUWm91yHzBWVNrkcwzYxeEXmupvxOrJ0jadXdQO6fYxqxiwMldVW4eqGAPYn71ke4kVh16EPR/SSpSk/XwA5ltmDIoBW6wcE52fE77r9bLD3tC01tP9HCDbWN5GJ/HUgOWZCj6WCUVhtQA90Tv+wwya/32cZfNVV9YkVdbek+2MkqmoAUnV9roX5fEmqAaZLvmeaN+hg1oFYLVmYZb1rltErwPh1BnPphWRTyBtnsnFgoyumdF6PI8Au2JTBWAugxWwoelNb1LnXn5Qu5cX5fEO8ADJptGAmWfSLNwm5bJ6Ck+KDX3ajo+Vp60LwIEJXhbGY9rXXoU1eSpUf9NDDJhnZTRqY1h2u3MWPbv76vtc2F7NgNmF3zmHqq5JhVxZWQ1HNyVvm4rWY1M2zVMuyi3OiT3KLPFmwHYm+bDxzgwDVtnK4OMCsGTBiJWsgHyDTYfBUiL8xmteVF/kAtNu3hx5VSGBiybb5uYNwc07Q/rfsOKripMAutwEYBfnXhe2IOT4+5AlQrV6GzVgZw+7yZIS2WOapbrIU2FNSOcmGmvauNr8UhSbtDJZ4nejGbAVfjllpsxgRbU8heiTbthPtpeY0frvcABqUZB1iwGY38INatE7yo9t1TkYDRiotI3Z6Zl2GT4L92Rjkxn5khSknwYsT3UAtbDwS1tIsCJdIcRSBqwPKCQwi+YiA5ZJP80LwIFU51nSULW91V/1HsfnmgED2GHfYcCMqbBPAHYeYK6Rs2EtfMayCQIX3fjbxt8LbS/ZJpMtb3vvh7l0oBnIkABMsw6V01/1A5cOKGi8glDTneLcIgOmPbT8GDC12PVzDJhm9j1Yh+ooDZhsva5jkSVKe7OMAfOpjEbNS8mCRc/evn8wXy/RcBkBu0/w0ZVGe3Rx7nVlYrr6GkzyVDNge0zydGDAjAbsiHN4yafdzU9+5SepfyQJZBMKWdmNZVcfPRbdsWF/1houYxljUpBeekRQKcj28pwGLGRDUhc7/OwXb/CzP/BNw2uVvzWNMdpeloKsPQrdHn5+ymW2RgbsLEJpwBaaKHu6LQuhd3kwP9Ho433Sb6V+SA9V8LWVVwqyzNTx2aEUpH/wsZfu6IonR8Pl2fLDoBYl+SEGrF3Zh9GeQ7LFdEGLJz1ZPFeLd56hWqfxdKEHdR2vyMMBkCm7P675sD2HJakvqb1qivz4RUdZaWyo452xKD3dy2Fw0zfaIbNjVgGYLwOm/m5KRa0ZMN+KqTQZPHd2xJ5lwBrPNkAAWA3YkhSkx2SdZwlX2CBZqMjtm8qbeTHP9Itf+au89D/8EaAme3ViHprIJQGYsdLweR6LYsKeLC2TaYkH2192dQAmdK/F3n2mAxiw2bLKMynJPFOQeZqwl2wd0uIlfet9H6pEMyYOi2csTbwau2eHdZ0mHe7FgBVHBGBtTeuxsZzkKZccBqy3DNjxAdhNmwX33nV+eCGfUsgZg63f0U3VF33A1ImcY9oPDNZ+3ZKL1u951MeX/T5954roG3oSSFcH01JvBtz1xTJgHil5pQfcOFwg5VmU8tjbtri/m4wasLOIXbFFTrM0+PCZLKv08CTh69YMg5fYMg+rWmaUqxiwXFVipovd3s1u1yfdkC7x7LHHezJgyYSsr+Zey/AXf+8tYcB8F70sHdyOt8WB3akZ5sVnp5enCfuUhzx/kl7rDFbQ3OY+HnrIWxXArer/V6QJD8pNNfaccxgaKPssmvNVV12vAqjMsxE2wAHqc6bUVG2nLSD8fMhgMOXd5sAyYH1zdMpkESLN2JclSTOMhabtKEXr5QCfJwkHsiBp5zcDsq282VgTgO2Ifd72IRXI2bHocR1Nb9Nmd1gwWq2l85lTTI9Yy4AxFFP4asBMCrKvHVNcY+Tqo7tJNbNfO8+k7Yfpx4AdMIFm3pQ3lQ0dfvehSzXL5T4PAWOpzfV3qIZg3LDiPkFokpXsyXKpDYWPr56x0jgndpnmKSaVnEr/6mwA8g2KvqaXEimlbQO0ygfMjpNig1JWCxqw1kt/pT5fBfNpX81VcvoymX025RVvmPGKH3i1fc1URvsUpaSJYE9OkPX8WFKdJVangj/m1i0+2m7MVQSfJsYAzMEs0ZoMd6IwAkGfEtnk8CTh66QPWrzNFFEvpN96owFbxYAt98sx5+AzwA9Ss1N0dhhWA+a3S6pFSbHAgPmKXQEqoWh2V+9hF70VlZR5KthH3YcplWXAWtOzzbN6bl8WyIUFQ1VteRRDpAm7sjy04BjWolhhR5Kngou9WjR/7Ff/eO54wGvhrxYYsLaTHDSmEbbffXz+x98NwFRUVG2vqiBFgGBXT9YTUdsALMS0MU0EBxQkTjrbtHNaJQkAFbwcUCIWKnJl569HNM/0hJpHnNfzwwrWwkW6eV69jyPeNqlcn+NVR4Et6x/lpiB9TIEBEs06yHp+XvL1EbO9R10fMBvAeTCRaaLY1IUNTdrXtJ6V0UsDsADxt2VenLHQWjbWPxA+bPFTeW1IJnnKRalE+CoFqV5flYI8fCITcjmj7yWzprdVzsuuwSENGEA2Je8rhwFTGxqf9Q2w6ewptX2PtPNP6Yt0wu+9r+P3/vQv7WsmFbzKYgjU3LhsLCW628wqI9aPuXWTS2zSLfYVPSWMAZiDSpiJZri5g1/O6omiS5cEcHan6Me8HHB44TYVT+UqEX6WclluHO49F+D5Y01ElxQSJJ6TRC0m5C4Dpn3EfCeZ2gSyDnMh+oaK5Q7sLtQ1NKmzmkYHcUYrka6wsQAoUsGMErnkIe+TYqWWL88Eu7JQ99FJIQrbgHkFA5YlPKADsNf+zlvYrdQkOxQSePgWlfMi/K6XVnDrE7wAPPNJjwI0A9b0dH3v75oNQwBGxUynIDtPY2MwAVhJ0riLpr99gpmsFy1RRN94p8Ob1ATzQwCWBGjxsg3FgLWOeLvte0rPFKQQyhx5Z8GGwngLejFgpUn7zM9LPr0oAZLEBFCHGTSf1FVxxLzmqyGD5QFYiP2B0Iv7b7z5r/n2//JmQFUwgl8QqfSEmzzwwH08+/t/g6pV41l0NTJZfbxKQW6yKSpyGqsBy+TRrb2WIt+g6CukRG+oju7NOucD5lRSFrKyGrCDuqUQndfzaI4HvbntBx2bb3cNqdlYKYcUpq2M9tDypYkZS0uyEx4i/Fu3VXP5pB41YGcOzZIArGkqKpkzXdEIG4C0oEfMBQ4De+RH1VcyRyykTEy6YRXzUmQJl9lSjayX6Nh82r/MbMWTy4AZs0C/h6xJNYNlj/dnPcC5D24A5tm6JUsTZlIdPxG1w4CZINRnl6VSV4s0d+JZSFCmCXu9/q6tex1Wt24xn3+xVxPdDnu89YOK0fTVwQEURcFlOeVCor5D20sOqlbtdgPTDRMq6m4Q4fv4kAEIO1nXTgrSv2IqETCTBUl3OA3rM5azJOFAlqrSzDF+TKVn7zug1Zuqqai4bUdd9xDWItdVjNKp2grRsYFqk7az0NLJNrL2CKBSHYDNMbp6M+B1fKIqYueZfXUNfOxEikzdh0MMmGzojuiqsIg+O7y5lQHFQWmaU8mct733I7z69/4aUB5e6nAfNjXhClPe84EP88779njfg/q7dH4mppM8YY/heTLTszWoXrGps8in5P2MXkrqVgXy6o2Or4K0/Xv18d1CCjKG1e5sAFZ795e1Y97JbphUsI8tS5YIZvIwA2aqIKfF8YFsnib8Vv9xvOexf++QN99pYAzAHNQ2ABse8lY3wt4oPcqt81QFD0tSmD4Lt9kpLgZgJt2wSjuUJoJ9MSWTzRD0QFAKss2M3mPQsZlFb1XPNfseoiCj5T0f0bsME4R47rLq5PB98K2YynXaCtREZwKwztMEFYaUySIDlvW1F/uTpwkzmR/6DspHbHU1bJ4m7Em1sG2Iije/X1/HgGrWSZ5yQMmmUPe+6yUHlf+OX52IWrgnoqZqOtuL0jcQt8c7AdjQBNonAFP3MnWeB9+G5qDSRjOjMXLeQ1mi+I3FTo/Fkhpz14YAzIP5mExoZDongDeBrK8WrxIlE+vHpl4zHlqJRwCVZaXyB2zcDY2/hixNEiqKufSd0bZmPlq8VD+ThwKw1tucubfZhflCgo7Uiz3K9DlMHGNhI/4ufKpR9cI/0c9TniS0Xa80XD7PY5Y6zHxlNVzKH9HzeQIVQGkRftW6DNjh91g6zegArO3V55sUpLcTvk1BqrlVShnUXSNPU9UlxGXAdCDsUxk9yAqWyEPI2ViRJUoTwX/vP563P/Eb/IPehxBjAOagFodp7raZ0ZCyVa5+yPM0UcHDEg2Y124505qXRRd5TfGuWrgBmqU6NP+FV2aH0xWGPfIyMWVI2zz///ur9L0MalUB0NgAbPgOSV/Tegh2M6M3YSEFaTRgHrusPEsOLxh9T0oHPlq+LLECdvc9fFm8Ik0U44Ca6P7yI7p9SUDwMslSDmTJhl4w2l4ym5lyb9/d7pB+64wRK623FrDTY3EqBh+wENNGm4J0NGCGQfPR8tmUPtj7IKVUFbm+AVg6fw1A+cH5foeNQi28rgC+a1sy0Xungmsx0X5s8yJ83wrCPNcbO3czYMv2Vy8BacLhtI/eDPgsmnmqNJWLKflM+vcllUYf1M6fg2/wkiUqHW0CWYBq5m9jkSZKlmCMiYVQKcDCs5fktEjZN8y8VAxYSGcJi3xDabg0A5YLHYAtGUvJsvUin1p5SC91FaTndzDHg3oe2l6x4oWnNyCoAOqOnYyHXxh8wEwq2CcFmekN9iJJkfY1pPnKDYlhfNv+9NkvwGM1uIFwFAPWkLHpkYK8Y2fC7uWcbH8Xa7GoJ+tystp0UaW+StJuYafY1947xTYplVBkLg1q0gUe5db54QCuD9DdqHMYFq1eStp6xoSAAG4JAyY8F5w8FXSk1DJlKhwGrPVrhA2DBmyOybQ92zwXfu2hdSiI9DSuNMzNhJqm1XR966+7meTJ3I6/7yWzmb4GHuNAnciw2+173YxbdN6mjV02FEPMNANG76/bSYRiHdyuCn2A6aNZdAF7L62VhueO36S+pmJwHw+pXJvmqdITOgJ4yygHMMJTvWi6qSufXpRgGN18PnjRbbVKHwZMCKW/nJsTKhL8Fs0i0yl52au5KCt48/svcUfn2R+WoZ2SG0jT1fSebGymn0kTyALUmhGeeM3Ngl3neep6aTVYPprMST5sqibUPOhsaHz1U+pEVAAlpbStlICVKcjh+A0yXSDVdD2zxr8bgDke1PPQ9ZKZZuF8daFZmvADL3oYN/+NZ9rXjDVN4TEvpUnCTBbzhTV6c+xlsq03HN0ZCcBGBszBMhF+16hG2JseKchveu497Pc5H33QEfjpAGzi4XpdaPbGinw1VB/FsB27Gzz0bU0jU8rco1poidaiswuGZwrSVI4JtVMzFLN3AGeZyGHhVVoJn+BFDWmzWzViUxNEZp4LxoFc2GXZNKy/iF99h4gg0mhmmNda2PYxPgyYXvhLBgbMVBtlHqyFOhFH7yElbS8p8W/c2+sArKSmWggivVKQOoCa04DZbgCeRS1y/pk2TvreVZAOA9b3krbrlZcaeAXjG0XKTOZ85IGLvOaN7wWGQgRf76VGTCxzY/2jPD24QKXLZrKcs+PwbSwP6j4siuj3Z+pnH/aoSBP2F1LyL/jh36bAn4k0Y9EcX+lG2v6pL8NgDXOreR58dGxm4Z+4jHKtgxeveW1eGiGlSkUXIpQBMxqwwU9OneCyKsglx2eDRVDX6yBQBjTjdhiwppfMdBDqex9zq+Fy1ic9z5cTvxTkjMUAzH9zbMa7WRdOG2MA5qBekvrqdSNsnwDs1u1SpUzmaHL1wE6nfgv/sqqtTPrvMLolWomumekqytW3W2SlKiRw07ChAVRqSvcbxZzU/lWYAPWSINK375x5wAz7Y6hmkwb1YQGXls1bLzTfalYjwh8WPd+00WIlp0l9GSsOLzf/XAWRE2km296mXPJADZhiwCRtr8XjnseLpKCVCVMniAzpqpAKtfC7GjAZEMCZyRoYArCA/n8AMjNjWel2atMPk8RLe7RRZBxQcvHyJf7Ja/9MvadNo/ozYKVoSemsCD8N8F4y0gZ30fI1FQZ9H2QxtyHaP/BfNIssYb+fvw9AEBOJNfBUn7tXtdoU2O94Y2nipiDryt9HzKS+pnZD0+sUZOO1MW26fthU6bFkfPXiAjCVgizQzPISRnmpZCXfIJUtGa2VFWQRDFhJTddJKsug+TOR3/ErH+WbX/X79jVjTeNDUmRGlrCk0M1nQ2M0jyMDdgYxaMDc6NykID2EnnrH7qYQzYIxnW4cdZiFTUHKBjq9s9GO074TTZ8eZo8aXUjg8x3yLD0UfJjgxVe8PbAGaqIxRns+Pi8A3RItXiL99B5pIpQ+Q5Z64Vc7HaOf8g3AbCAt5wMHn+BDifDnU1+gUpA+LF7h6NgmHGbAEo/raET4JmWiGDCjtQjTe0x0Ktmk73xTkEaPZ8YB4FTU+qQgVTukzE3JhwRgiRMILzJgvgtOUtJLoZlI9KLnz1pM8oTK0Q5B2H2EeS8ys2yoCkJf9ifRgey8HtG7CjI1AZjDgO2rnycTv3lttz9clFLg14wbICnm54T9uiMX/vcx1xWxEycF2do+in4buwOHQbO+eqLz2lg+7dE38dmf8BgASsuA9eR03nO7+iJTsn6G9BDhLy2wcJ/pXtK1rWrx5JndsAyYUBqwWdNRCP8ALk8Fb/3wjD/766EzhAnAfGQ6mTFX7p010q5PfpsyODsasDEAc1AtEbD3bU1D6sWApYmaqNwU4kxT9VsbfjqDgTlxypzBe4CbtI870TW1YsB2pp7sy4KJqNWAedoX9I4GrJPS9vry1R6ZAM7VrGSenkFCCDXZ6t1q0y0wYL6+RbJAyG64/qaQwbMFzjIRvq/vUe4WEojqEHvkEwCVuurK1ayYHX/uufCb5r/GdNEIbv0DsIF1GHrfGUPd1dfBpL7mNWD+DFqeCicFqcZz3fYUwr/5cJoNY0lqBiwkABNC0KXzC38fUMkJ82lQE4ElfUAApn28RDfPxnpXQYrDVYymoGPqqW3dXWDAEnpSIdnr/PyvsqygIbX3cV972vlrj4TdDBg0AZYoWSqoKJiIBkHv+Oq1XsUUaSJ4ybOeCGgRPlJpKmkHo1sfaBE+KCPWQjRIsbwSdOmtdQtrehnc5xdH19lqM9gQG4ssSehlovSAGiYF6RtA2bnVrpE6APPqD6tCnnZMQZ49DP5T8ynEmtxLhJ/pShmXATvQVP3mxuqdYrFs4bY+YqF+OW4KUTFg2xMfH63Dk21vjVw9AyhHuCx7qAIaMMNQRemeQxaw488cvYcR4dtG2B6alTzVZfdwKBD2mSQKN5BesNLw8T3KU4FECaddBsykIH3MZKeF0oBNXA2YuQ8e10CdcEKfKiaxl5K2bb13/DB4srkpyKSr6USqGguvQCrUopk5XRVCzDfdiliTCg5NQeYO82Erz0K0S6hn0rU/kAF6QhgqmyeislWQKpgPSEHKkmwhBVnJfGVbLRg2lm463QRgE09pxZXOMGA6Da6Zm73Wbwkqc1XV+9GLFzmoO/bqVqXfAlJfbgoRBmmED/uTJslcc3rL/tD491GcY5QVGxuSvlPnOrChRn911NpwVAoSoNS6zqEoJlCEj/JYNCJ834KSLBUqfe/YUITICtLksKzABmBeWr6RATuzaEV+SP8kNVW/4WFDoQZHPrdjP9AT1damB1WfJEcOLt8BblgLN4jsmopaZuxMPL3IFhiwrglLmfTpMEl0UlJpBiz3yPGD07h8jj3yFx1nesGYiMGGQupChFWNsOH4QNhnss0dG4n5INKv7D7XrYpmcp49sufgsWBMdCHB1GHAjJ2ITxrWQGYTJlR0PfRGB+e54OS29F8d3/WSRLbe5puJUIFD6hqpBti6ZEsma+XB5e/8bdOoOog0KciQtFGfTecW/pA0LAy6zqleuEFvSLwtGBIqchKnO4XQDJiPj1iiNWByyZzgZcRqUphg55XNVD2XvgGYScv/6pvezb/4L29mvzILf8hYXGQiw1OQMHhgmSrIUF+9UqqUfKtF+KEpSFDp25m2wTgqAFvOgDljqSfYJJusRCKYiMoGoUEBWCLoEHMM2GCo6zG3zq2RejwG+Fya5vXXvQZMCPEdQoj3CyHepP97vvO7Vwgh3iGEeLsQ4rkP1TkEQwhqMa91EK3ybvLxyzHO21nn7hTVz9ubmyuPTxLhWGEsBGC+ouH8MAPWaR3btkcAZnQzbtm87MzC7cnCpUP1m7GhACg9tUfLCgmyAPdyE0BNGJzwaSuvPoxgUlfzD7mtovQMwJbZUPgaTxrD3QOtHbItP6wRq48NRWqvAegATN9T7x07qv9noVunyMYUY/gdnzqp4F5Kq1nxvY9JwuFihoA2QPNVkE4KMnDBOJCqmtRUruWBi2aTlHPi75AdP7heZNpBXZfde1dBpspGInXMmVNPPSIMfmxzc4rVVHpWFS9sSG7Wh13xDcD0hmIiat553671r/JOJetNmZuC7CILOiY0NgWZ0wb46ql5zdWAFTT+wY9zriWN40O2fBwstaGwsgLF6IpAiQtC0OkNRRvR3ixLE26/aYfH3TyMPdlWivjwKGpJE3GosjlE33vWGLCH2gfsB6SU/9p9QQjxROBLgScBDwfeIIR4vHSbQ50SBIKaksmcAWeNTLa8jjeVNi4DZirPzm2tZsBAlZyrH+aj+1CR5LJCgpumHjYOmZps+2Yf8ziYBcN7otHnWoiW3kl9+ZSsg/Yyg4WKqYZ9zwWnzFIOqkLvVPVOq1davolXAOYuGOpeVtUBU/wKEZQFxHIWr/PsCQpo36LqcArSIw2qArCCDaH0Iq3DgIVM+DItKYUKwIyOzjd4cQPhTutFSvwLSlKHdaA5gGJzaMrum64Q8/ehbltKEbJgKFbbaGasCD/gGjainPOfCgkiYd4MVjLodnz9o4QQtCJXLZk0EunvI2bmNRpdlCKEreT00XXOVQXreenCRMIeXGn83MjNWJpSccfOhIOm44JovMeieabdQLgPaAyfOSnIqah08NFQiI7G19ZFBz+lVD5ezRopyFKoAGzzGBPVo4xYQWvAOumYCgek1NOJCuB6yaztg2QJeSL48s95Gn934w/sa1KTHIWH0Xi2RN5R1xUlkBceekSrATsbAdhppCA/D/hpKWUlpfwr4B3A00/hPA5BCKjEZN69vPdbNGHIT7uaFbPoTTzKtcERoFsGzLAenpOtDcAc4XJbUZN7MWC59ruhdgO4mlYmZJ7nIJ1dWieHhb/wEOwCJGlGTTYXRGay8Taj3SxTKiPCNxWErV87JzB2IPOaFStg91hwtspsaQCWefaSNCxdtSBgN3oNvyrIoSfmZtLS9f2geQkJwLKSkpaul0gzFj0ZtG/6rMdzfnubnUwFcINnkCcDJg4bqQ7pO08BezLPpjaNTqP6aoeSYeFXNhRdmHElqrn8nAas829lBMyZX0rJEIAFsHA1BamTggxx0k8TQSULBNL64YUUtZRZalPy3/e6N7FXtVwwh3kGwqW26JlQc/vOxArYfVOQbiBtKhlCOnS46WzjAm+qir2NjZMEsgmlbiVk0uG+nSXUh6m/LWmUiao4mgU8qhURDN5+0mrAAlPqotZGrroQwXNOyLQ8w/X2o6tpQuQlC5tjI/MpPDIsia6Sb/sbQ4T/MiHEnwkhfkIIcUG/9gjgvc7fvE+/dupQAdi8/inp/SttEqEMQPO+GpqN6smyCLRwsAt3oABeWL+c4TvItva20sh1lYlcooMrMr/dqsjdFOTQ68s3CE0SQbWQ8shk6x0Ib5WZTlcMDJjo1Xfwaec0r+FS19H0jfMxMd2eZHSkSqOzEER6taRKXS+zIY0quoZOCtJ0dSBqGDCArUSlC/qANkAWaakC6X5IQfpUggLcvFVyz123s6FF/LMmzDrAOOEDhzYk/um7BQ2Yacru+TyaczCFCHWr+2EGXMONjc055iXESgMgLZV8YaIrMb/rdX8KhAVgbVKo8acRUtRiqyBhSMnr71B4PNPGkw5gd/cyb/vQZWtm+7LnPNHrHB5z6xYzzehe2Ch0X9IAEb4xoxXKTLjvZTibitlYqmey1vISH1mCRT5VDBgDA+abRlUf5gZgxofsKA3YcTYUisFKAvWIoMy6zfGV3lT5F+YIXv1f/5CX/vzFYY0MqOjNkoRKzm+OjSWKT0Wueg9xZlKQawVgQog3CCHevOS/zwN+FPgY4F7gg8D3R7z/PxJCvFEI8cb77rtvnVP1+zyEDsCchb/3T5kIoQMHcDQrNZXMyDO/cmvT/HexxNZ7h1GUdAgevHTJ8Y9SZn9ewYcuu3f7xqEDMB8dHLgMmEpBdrV/yw/QrU9EMVdIkNN4p1w2S2V+ueFUQfr2YQTtmr2w8JuHfHO6+juYatM2mcw3gfbUHlkNmCx1Cxxj4eBvHaCa/6oxs5WodEMXkYIkG/RPQxFAwPH5VJk2OiXrvgGYTX2BbQ4fmjLJkoTG0XW2VZiOTSKtn1rnmNGGXMOnP/5OCtFxvlT3TQSmILPJYIgrgV/4o78GCGri3IlcFTNo+Da3B8cJH5boblYHYFMnJT+hpsxSmw6948K21zk87dEXmMlCaZ6Apu+9TVBhPnU10T1iC3G0h9YiXD2hkQWYjaWvv6F6ow0KqVp7NdrSxLu5PQwpSBuAHR2ELhfhD1WMvZRDWjqYFW9UZXWnNiS+m7I8SfjwA1f4X/f3w3zSBegRXRsK/UzvHagxOfVwGgA1J5wVEf5aGjAp5XN8/k4I8W+BX9T/fD9wl/PrO/Vry97/x4EfB3jqU5/6kF8xITgkwk9lmFOxpVLbGRQbg+Ghh+M0aB+vBpsClF2NwJ91KPOUmSx47f98B/vTd/D1z3kcoqvoPXVsudE6uAxYW1EFBGBmUjTaIbPw+2rATOPbuUA4IHVl0hUFra2CFF3tnXIpMlU1Bli9jnnItz2qWY1nXJOUlDpwABVE+iwYQzulgh325iwcanKmHgFY6bAOG4maLPsYDVhWUogDla5ow1KQgE65qCpIaxzpnYJksAMxxSiBKZM8TWgpyPXxRjzu7WnXS5v66qXSjmyIzt/IFcgnisHKdFeCkNYp7vFToQLhQqjjZUAPwUaU5H1tNVwhVcWpYGAd9IbC6EJ9CmsmeWqfp4KGIkuUkSZ4j8WNIqMVuWKv5OCh5Z9KFnYslbpDR47y5Us9NqbuZsAI0OsAI1eLfEo5M074MsiTTn2W+tuCloO6o0yOTsMuF+Ebfa5itYf74P8dZDpR7cU61ZpLdcfw14VK9Hl1FeQTJfPx9rQTw9ysn+WDfTU3b3p4bYIaC9d9KyIhxMOcf74IeLP++ReALxVClEKIu4HHAX+wePxpoaYYUgT4m2caNGJ+wZCdavnhG7xYJ3u98Bvtke+CUWrmY0rFH7/nQUAxJ94LVqaCH7HghaYYML8gMksVTVzo1JUJwHwnmkQIavI59ijEe6nMUmqZkYuOXgcNom+8dQZFmhx6yPdNALa1upo1TxOmearGgjMOUqSXgN7YUJgKQrtb62vVPsZjwTBBKMCmtlCQEYLbuRRkQCNsi6xU9073klQMmGc63YwDsGNhWDD8mY/dNuXP//ojNF1v2dg089sMdFKqsSxa2z6mDK1cM6yFVN8hJPUFTgCGEm9bFihgXrKpWFOyLxtkgLZ18XmwfUk9ruMkT2lJ6aVQBRBCDPcxgE195pPutJXVjW4J5fP5oL39dBBZ6nRyQYiVh5hj8dq+jypqIZ9SSOOm3wdZOKgTMSL8mlnbMznGhX7pNJEOKcxKC+jd1/3OQRXmdH1P03YUoiPxvA/nptrqCYb+ul2Yzrpe2BzvHyiyYtuTAUtTcX0wYCvwr4QQ96IUj+8GvhpASvkWIcRrgL8AWuDrzkIFpEEt8qFKCcgC3JZBaS3UD2aybYLSd9ZjSQ/OtlFJTV+vGbPwTkVtA6akb/x9j7R1wHwA1tBI/++QJgkVmd6tDkau3pqXBB2ADYFwiHi7dBgso1VJPJt5w/xkbXdZ+iE/52EnArA1yfR3UMdX1YwJfqkvc9+Mb5HpBYlOo/p4NwkxUPWbiXqPENGxRTaxrMMQOISlTAppnPRN+s7vGoKzoXHuozoH//FckfP2938UPnjFbgZ8zGwB1X7IjuWhHVMY66H9nzR7N5T+e47nqWKvVSHAYGJ60PvJGsDx1mtnkCo9mD8jnRxa9GgrpUf0sA6YFimggriSBikliQy7j6Cq3Ewg3OlgXnim77JUOCyc6oOY03rLGoy5M+gqyE4GGbkObzS1PmC1bkXUBR0/BFAHtQp+jhpHSyUnzvFV06nNBETIEpo5XakvQXB+ww3AdAq3r71lPtnc3KyOH6ye/FOQZ0UD9pAFYFLKLz/md98NfPdD9dmxEELQkEPrpI1kQ++5cAO0olAhp10wKm/dDkBmSmn14DI7dl+tQ5mrKsaS2n5mFpBuMH0MRd8qwXOaWwZswzMAMzSxWbTswu/tH6UnS8OA6V2/70Na5oOTvSkmSPpaBdc+558mh5gX85D7GOoCbJcZdTMEYLMDFYD5CHaNBmwm8zk3f1r/aiGAT33CnfAu2EwamrZ3/Kf830NkhfIB66UjgA9YMNJC6ff0ojkNEOGDE7A6DFiPIPFY+EGlPQ0b2zishW9Kv+8lFYVlAZs+rPoOsO1fCvSmzKZR/d5jWk7opVBtZ3ACsM4/gWHNb9sKcnX8xEPPCGpDtMiA0SlG2ecuTHJT1ZvbTVkWGEgDduHvJZZN9fdzS1RlNcrHq9f30ZdVz5Khv6sZCyG9JN3vkMuLNp1d0DKLqIIsaLlkfcBijm+UhcQxvSSPhGbF207azbVvdmOSp9x6x8O4V3xkWCMDZD7ZHBurjp9V/t1mzHuMrYjOIAR6x+0s/Me1eliGViwwYH0zLOYeKExz24Vyb9+JptTNtKfUZHohD9Gx5enhdAOaxfPVsaW6b5pJQRrBLp42EokQapdjGBe98Pt6L7kpRBnFgA3X4P/3KypzbqogfQW325NMfQc9DvZ1qbTP8a4GrNDpOwACxKoAX/Ns3XsuadWOLyYFqRmwTuL4V4Udn9Ehe3UOoQL2cjr/PNgm1B5pWICDuqPWC3/XS+v95NO2BFAGsuTajHZIG8WkIE0VYqjuZnOSz7FHJgXp20cRnGenrawD/IZnAJYIQb3ACBttqw+muTrP2tmUxWiPyCZ2M2DYVN9U8rwGTOn5SuHPqrtp2JKGpo9nlDOp7uOVg5qc1ss+YTjeMFiqFVEZ0AjbPddSNLaC0X1fv3NQ96HtB11oyDn8rRd+Lj/4PFWgJKUklf5Zpvl0uG4vZudW/w3+WWHAxgDMgRBqkujqmdrx92pwhpguDlT/kN/2rb4DKIv5FGRn3Mc9z8Gk3woaMsuA+U80bh/El776d/V3UL0kJ7nfhJ8nCbXMrAjf2Fj4Lpo2z+9UkgIBDJjSgAFInUpNAmwshBD2npvUownAfBeMrUnGzAkiTe+8zCP1ZQIwtWA5GrCuDtIjmp3xVLTUXR8ZgGkj1l4Gp87M8aAKCMyOP+Tzpxs6XdktBGCe2G86G7x0/bBj9/Vu6qRUKUjRIrtOMS/HeC8thWEd9MIrAivPNsrUfgfAVu8ddH7PEzA0fG4rHrisGP4tX83MEg0YfUPrubE080Ylc60dclOQYWzqIRF+gP2BTUGK1qaSfb0Fs0RYBq2koev6YDsR9UYluVTFFBf3ZsoWw7c3K9hgvhCtbYUU9DwKQZ8UlLRKA2YZsBAd2zAnmKKWEC1fXuhx11W2H6bv3K6yE/qeOfIOwF9TeYY0YGMA5kAAl5qEB6/s8kO//pfDghXit2NF+Lraqq+9y70Btia5Envq4CM0x17mCbUWDZsUpEqj+g7OgT3643d9WH8XVUgw9QzAUjcF2QNtPVwXDygDztxh4MIakqsgVP2tMCmn3t/GQh0wpBsAGt3P0vchV15kqb2PB1rE71O2bzRgSvzd0XdKIim62juIBOxkvZG01G0fxTqITLUi6qQj4o/QrKS9Eu0WNN6Va+AECUYvErCZAFX0ZzYkbgDmYyAKOgWpU09CNjaIDEpBGuGzCT4CvZe2ysx+BylhooXTe53/vNInQyr3Ixcvq/f1TKfPBWBmXuv85zUbgFkWT40H9eYRbGwnabuGTPTex28Wmd2UmSAuJAWZJG4A16zBKKsArJeSy7vaWiVEhO+I6I2xcdDziArGDYMWYsVhINJBA0agvhfgda/5af7e/3MAbcWs7oM2ZYkASaIkNXosNoEG02kibmgn/DMLIQQX64SSht95x0ejBpepNnrPRx4AwhynQZVbu+k3k74TnqmvMjO75dq2Xcho6b3L9oVTLaQnyc6/H6Z9D5u2kUGeQ6AZMJkNaVgdRHkzYI4IXzrMScjCnWUZlcxsyX8b0LYEYHuSc9Dnh3ZpPgGYEII8Hap9EjmkUUP0iGZinoqGK7PGlt37MpGgxp1NG0UuOABpXw0pyIAFZ9MUPRjmKpABg3nmxaakPc+hk4P+Ketr6wMWVbmmA1nL/niyLxtFar9DLyWbWVgjawCZDgHUg5d3gbAAbJF1EAG9JF0NmJkTMmLY1IJESJDtwD553ocLmwVPfawqzDdVkCGFPQqCSmaWTY1N6eeyRgKXtbdgzPOkAijTSzLseejTUmnPdFut4HPIJ3Ysx1yD3SuXed/lHtoZM2NN43kfhRCqoXeSO1misHU6T5Mbxgn/moMJXrbKzGqPQjRghqH4zp//E0AHYAEasK0ypSazruMm+EgD2B9VtdWSaiYlC6Z41d9uJtrCoQsLXkzVlHlI0wCfF3W8UOk784CZ0vcQG4qFBUOV3fufg7kOhgFLZUNHqtqJeGCrzNjrhyCysu0yAsrm9X3I+0E75LtjB5yS9ZbLB23EgqNsBgoa+q5XdiYQLNgFzQR3UpW9B6Sdtje1f53Wi2SEjUWYZ8Ck1VT6a8DMWEr7yjJgQaxFZrybBvanwT8Q3iozOxYlME0NA+Y/fVt7m7aiMzo436IYMein3GIIX1a7SBMSMa8BSwO90NTfqu+QdjOnnZP/fXjFC+8FYCvtbRVl0PPEwOK1c7YuYSn5TDNgu3s6AIsI5gudQsxC9Yio9eyQBizgPYQuhui6PkoHZwto2pq6NSlI/+OzVNCKcok1jb+eb0xBnkEIoJIFhejYKdOo6N4EYGZnkQbsFAE2dLqhrdWCbQa4vw1FahecPBHQtaT0/tU+jlbi0efVeYu+DkrfZZpFMzqBRPr77YBuoyMzy15ZBsyz5Pxz7304O1tq4RZu6irgPhohvxE+Z4FM5iRPmTkBmDFt9BXcuiye6E06O8wSxSxuE9FwWTNg4QFYSSoksuuCq/fcc8g1A1bQBAUvO9p3ra1nyrgy4jsYGwk3Bek7WUstwgcVOK3NgPWStK9pA77DRpHNpe82UrV7r6W/CD8rh+rqvjOsumc7pkQxP+r4YSz6zmtCCGvGali8VEYwL/o6ir62zcBDAwdApQB7paMK2ZSBCiILXQEoolLySoTf97Abw4ClGZJEBVBtp1o6hTyPOEaqrWbQAs9BWB8wtzI6JADTYUc7s3NCSBCbJYkK/s3aKBs6EvCsjFZGrGMAduagRPhqUjlfOvRqQKsIo7UoGUpsQ9ifzVKlII34ftCA+dtQVKgdTpokjn7K7xwec8sWT3rkbQDcXJo+ioHpOy2iN5VjSSADdstWQSVzywJ2ZpflGUTevjPhR/7+M9S5zxlPhjBgKu1S0CKl8oMLCaQnecJBnyFtpY7pBuBXeTbJBxbPlOyngd9hSEG2XD5ogqw8DGzqu5utlYJMZBNVQXjTVkklc2YH+7R9HxWAGRuJdi4A8zuHb3/hk3jK3Xeor9JXNG1HLrowDZhjHaBSkG3YpqwYRPgSyTRRDFgdUNxz8znV8qdrKrtw+RYSKAZsXoSf9GGbqmmu06jGhkLG9CVV55t0tbM5DquiBGwaNCdsUwYDA9b1LiMcVoWY6+++tx+mKzXotAZLiejDU5AyVdXVSkNmjFgDAjA9J8iuipLpJKkKlJr6QBvqdkHHp4lQGxjrI+bPxoLyEhsZsDOJYaI5X3RD+5MgBmyotAFI+zZot7tZpLYSEwbH6RAj1lrrFPJUOBWEft9hWqR84/M+DsD2CUt7f9ds0GZ5TuVZJsME8Ddvluo+6HM3i2YQ+6P/1vTdy2m8g1DQ7YhkbvUiqWyDGLCpab/SzTNgpWdD8jJLrPjbLFapDJxsbcl5zeVZq3b8gZO9SdWlfR2XgjQWDH3teDf5By8XNpQFQzXbp+lidDtQy2ww8GzCtEO370z4e894nDpE1jaF6fs8qj8eRPiyD6/k3Cwz7rjpvDpeYgOwRvoHYLec3wHg4pUrwTq4Ze7jSlPpP5bUhiK3NhLrMGBpVzkpyAg2VippREHghgZsV4S2jzcmzmRD33XMDgwDFqjhSpTFT932mgELTEFmQwAXJcLXAZhoK4cB8z/+nic9mU+5M6WeHQz9MAPuY56KObuo0M1xeiO0IroWIZzec+cLt4WO/+DonCoViGTAyNkz9LR2nM4yvwE2iPAbVQUZWEEI2Ikq64eFP2SnmCXzrtdpYAry5q2Cmpykq0AO3k1R6Qq9YGSBOgNjx6F8uMIXTdv/znQ0MAGYpwZMBbGD9gi0j1QI8yKErRwDwv2rYAhU2pnTPiamCrK2pf++Fb0wpN/6prIMmm8rIwOj61T9MMNK1oFh4ZfNIEKPsOIw1aRKExl2H+646dwhF/lH3HLe+/jbL5wD4IGLTgDm6YWWuAGY8WMLZMUn+bAp6zWjrN4oXEuX9HVwQ3N1vLkPtTZi7aJSkBNdBTlUcoY/D1V1EMegoWyRShoSehL64CpIUhXAVU2nAuKkCCrMSXJnbo1Iw/6Dl72c733ORAdgOgUZkGVKEzHX5i0NlIdkowbsbEIw0Pp53wy9vgL0HnIhBZkFVt+ZkvN3fuB+Zk2HbE0zb79b5fqACUGUlYa7aEIE8wLWCqOTMsiHDIYUJKCMI9dY9BLDgAVqJYzfjC1ZDwwiDQMm2hm948GVeS56cyJ87R8VI7hVPl5qlxsXgKnzTfpqCMBCxpK+5pmsaZuaVMggRtkEsn0zsx5cIWPpkTdtDIG0I8KPWfjzvqbXO35fEb86ftiUWUbYswLSfY9FC4d//5XP8D78jpsdBswsXN4u8kIt9U7lWWhVsdWA2U2ZTsMGLPzDWKzjKhDTgQEzLZ1CGeGKnKlolQYs0koD1IZsEMBH2EiIxtFvHX8fnvTwnbl/y2xCKRpmTZgHl4HtTtHUji40YCxoc+Wmng39MIM2+IkqbHP0vSGb4yy9AVoRXaswC7/oZ7R1T0mYT4thwKwIX4btdjeKlCsyZ0scaPNM5aTv2wi71DvNiTDtY2IYMJN2MpNt2MJtvJes47RskImf9gnglq3Ssj90lV00475DDX2nPYMCUpA2japSkFngQ17mQ8+y//h772QnUKzq2lBkvSk5DxR/wyEGLPh4m8qtdUVvSuZZCao+32FTuzA9I5hUbE7ezoZ0RcCC8cvf8Ons/tIbKP9kvnddlJN9Xw1dHSIqQV1GODSNSq6qUSVYBuy8R2N4g1s1A7a/vweZ1nF5joVEB0kyKebtQAIYZdXdIqNMFAuYa0bZv4yAYWPYVchWzL3mhTSjEymlUMUUG7RBsgTQBR26EXXeN5ASxcKVNE4AFpeC9LGQeNO3/a3DBtqp8vartAVEsCzB6EL7WXBjeYDv+adfz4+/f58f+oQDKysIuY9FllA7Fj9Z4OY4S8RoQ3EWoVKQuuKpq4YUZMDgSNKMTgrroaXYH/841zAfZseOdqH3XfTKLB3ahnTNkKMPqtqaT0Fmsglz/sbp+9aHWwfsTHKnpZPDgAWlvtTxaVcPi2aIW3MmLIvXWxF+GANmAqgHL122lYwhXjUmgMtkzX7dEtyDENRkqzVkReBOU324Zh26mlQGOvHDMJbkwB6FbGjKXNmByLa27uchi+ZGkTGZbpCLDtm1kQGYXvhlE8m8ZPRCeco1vdTVrGH30VSezVk4BDayBtT5Bxb2GEPnPh1K/9NA5uST7r7JticzfnDBQWg6MPNRekRUT0zTx7AQYdpWUBv0iW5FZDIEMSn5UjRKfA7hGi4twvcJwM5vFIcDMM2mWgYs8Boa9jdpZ1Es4O6VS9y/L+nqA5pWNRT37fQCxuR6MQAL04CNRqxnEIKBdRBdY803k4AHLE2GPoiADj78B9djb9uaE7CbNj5F5smAufYFXeVoXsL9dkzvOrXw+z+kEmkrCKu2Uzv/oDJlQW60Um1lS86DJgqr26moTUPzkAUrdTUrMniXNdWVawCbWR/cxsdtOpvJhgPdeDfIfwpAO9kDkQzakMpN+7Bm4O7xmayhMcyL/1icZKliQ9uZtYCICV5AF3PE9LO0qasheAmuXEvUWLp80IS3jwHLZEqpZQ0BZfcw6HbSrlLiafDuh2nUD30yFJXkgbKEb33BE3n+xz9a9VHUBqAhzxMwl4IMbedk0CXzQWDofagoKETLrHY8tILkHcaSpLbMdLgGTAVQRuZCSDocbABWtRG9JPXxoFjxmAAsTRJ6BG09i2pltFVmcxY/GWHZibEV0RmFy4Al/cz6T4UuWm7ftjzQOmCSpzzxrlttD0DRNdQyhAGbD8DaJkI/lTrVd1IGMy9SKj+1XHTMarVT6wMXzXsecSsAb3//R4OrtgBnt9zYCsSQ71Ckg5ZO9ioIDanktCJ8YDtrnQDM7xyKbDDEzWXNgZ7wfc0zLbIJhZ6ocxEmgFfHD0aqmQwr9wYGDVjfWO1RKANWUSC6akhXRO7YaStoG3pEUPAyVM/VTjumwEUzUYHw5YMmvJckg/mlhEE/FQJr4TAEoVnu34wbtLzCtHIKLGqBgbkxup+g1mAwsNp9HZX6AugS9xy64GBe6UJr1YdRtKozRURKvqSxfm7hTvbKZshWMMaw4qK1DJivvY+FDcCqKGuaVAh6hNJ16s1xiCxha5Kx32fDOA5Mh6fJ2dGAjQGYAyEGDVjS1daLKwtYtIxzdkljg5dQr5lePyCm1YMS4fsxYJnjZJ90VXDZvfrbwTrALDhBARhDINtUByqICbwGn/qEhwPwoQcu2bRR0GSZJDRkpH1FU4UH0nk2OOFbDVigCN+kgrfSLlis6orwC1qqplUpiwgGK3eYzDR4t2xSkJWykggNwPTxuayjKqYmmfKPEu1Mp43Cd+xWs9Ic6BY6eZT4O5NN9MJvKtcuaQYsOJWsAzBsVXHgfRDKHDnpG1tB6Lvomc1fK3LaeqYrCMPnNdKSREjatg4uplAnYu5D5WxowhmwUjS2+i40/WbkIft6QxTL4hW03Gzi38CxIC0DFlGVDKqZNnVwGyALR5aQRIjwk0T1c+yamZWXhGzKtifzXUZywmyO8lEDdjYhGFpupH0dbIIK0HZSe8UM+qvQKhOpd2md9pppAvowghNE9lWUlYarATONTn0rpgxMNWlbz5QeLnCSsItmO7MariRwt9qIXH8H3bIi4DsUaTLXfy8P1LGZsnuAado67TL8K89qpyensbGISReYAKwU8SnItK9VJWPwgmPYVFc/FVbMUJMhupq6C2vca2CDnV4FUOELv5OCjPA9As2AiZaLB01wNwB1DhPbWSKmHyao4CHtq8GcuPBkwPTU897LPX/+1x+J9tBCz6NdPdP2B3H3IY1c+EH16lVO9jr9FhDI/rdv/Aye/aS7KFCMdIwpsPXmo+GmiZh7zRdS93IcqiDDU/JqUxf3PA2FOXHWNM959rP5lLs3kE1ls0whsoTtMmO/SwcfsECddZoIulEDdgYhsNV3SecMDs9G2KDSb7aHYKAJqn0PvVvue+VCX5GRJf47dhP8iK5xCgkCJgqhfFYyWTM70MFLQBCq2reo79xqBix8khi8ZgYWLmyya0RBKhvaKvw7DE74Q8l6TNk9QNI6O3bPiSZ3UskltaXqw7VDpXXeLkQXfrzp5djVeuGP2y0X1FEWEMbXTvWSNM2Hw87BPL/NbH+thX+OxYtiwGqtAeuCxqJ7DomsVVut0BQkal5Ku8pas/iycEaEfyBzZFvpYogueF4zY0k2M60BC0+dgSqGGATwYYxubza3TXgA97jbt9ne2lIMWNNGdZYY+rPWbOqenr5N2Q2kHks2AIusjDYMWHRhTl8P9yFgfv+Ob/82XvoZt6u2WBEFVluTjN0us/NJThgjnKWCZkxBnk0MGrDaBi8hrted7h1X0sZZQAB9VljTRtGF+YCB8x26oYIwdKJqhWqZUVWqXUYakIb9xEddmGPAigia2zyQsqmQbYT3EtCQk/UVdW2+g//xxojVWAcozUqYf9VgXjkbduyek23uiPALWlsQEqpZUc7b6tgy0kcMlCVJLuvw1FeS0ZPM2VCEth1pREHSqV6SJU3wWDbBTjU70KxF+DUENdEPKchA7ya98BsRflbEnUPS1cGedAY1OYmbRg1oxg2qo0Aua90SKjx9Zz6vbzQDFjkWs76KNjE1xRC9Zk9khIC9kLVNQYY283Y1YFOr4QoMIlPFhppK+9DjRVaSi46DWUUpGu+eoBZuYY6M6CUpBK3Ikd1QYOVbEAKwPcmZycwyYKH3IUvGVkRnEqYZN6j0nenjlwUs3F1vArDa5qhDF02ZlGSip2sbkr6mlv4+YMCgAetrh+INn6hoK37gl/8cCNuh3Hlhg3/yOU9R79Mc6CrIwElizm05gsVjCCLr2T7gn3IBtwpS2VCU1HQBk4zpDF0sFAAAPXRJREFUfQeKiUxlrQIqT+1R7mj51I7dpCDDF73MMmDh7JG72w0VuwIgBJ3I54OXwHR0KwpVgakZsNA0qhlL1WyfQkQs/EmGRJDL2rJHod+h1/33Lh6ohTNkTgHsfUv7ipw6nD0CaqEZsL6ikal3IUKiGbCKXAXhbUsm+ojNgBOAifjgJe1rsj5uY2lSwdaOJEZTScOs7qJsLNzG7JPEBGCxKcg4I1fzPDR1pSur4zYDaafuQyvyoEKEz/7sz+ar/uNfI9qhSj+E5NgqM2qZqyC87ylkE/Q8KBuKUQN25iCEGBogd/UQnXu2jwGVfqtR2iHsLivwATPsT6saINeRGrC0qwYdW2Dw0iXKd+j3//KDQKAHFwNb1Vf74c2LGTQBohsCWRG4aDWJSqMaDZivCz3onpqO6aJKmYQxYLYatZ2RdjUNITR5QkNKj6AQzVCuHRFAGT83xR6FTrZaw9VXipUNXXCANinIZTMEL4ELRpeoAKxrGlIhg7+DYT6bymwGAq+BEPY7iBjvJ3TaSKzPgImuitMeATWFrSBsAlKYRv5Qk5PLhjamnRPY7yCbipI6ODMwdFVoBi+00I1lqgToZm4PHYtkE3JaDuomykPLbUs1sT5g4c/kOilIMze39QET0QTPq0NldBWlRzw4OOCgVXO71fcGnMP2JLNzq2xnelMVasR6Nhiw0QnfgcBNQQ5uyyGl/51UIvwdse+kXCJ3inVF2ldUbAZpwL7xeR8H/10zYK2K9EPTd21iHvJWHx+3cCf1FXV8IM09x4D1FbVMSUOsA1BVW1k/aMDygEBaGaFqHVt9wBZ1UOpqsYGx8tDyf9yKVACCxnjCNZFjKS2sTiOX4ek7t6NAHtiA2aDTwcuBZY/CrQMyWdO3KpUcumCYyb2uDnQ6PJw9UmxqQx0ZgBnvJlMFmQWnfUzwMlPjIeI+NORkUgnYa3I2PI8zNhSqArCmr+PTdwC0M8Ush7J4urI56ysSU8UWnArWKcjmIOp4E3S2dUWRxPi5DUasEyHnXvPF4SrIwOfBaUq+jg+YMmeOG4sgSJ3OEiHZCTcA62r1PITMS0+56zx7dRt2ug8RxgDMgRBD+i7ta2QLlczIM/+Fv+sdHzCTgox4wEAzYL3SgImAsvlPuefhKgDrKvpG7bJCNFxgDAtba/YXzIAZ48f6sj4+bpKwARi5FQP7ohUFeV/bCsIiMAA70I9H32jWIaIbAKjvIPqaOpABg6Ggw1D1Ma2ITLomjxCwu35qOQ1N6PGoYL7ATd+Fpo0Ksqaiq8MrptTnzVffBaedMM9DPQSRobICvWiqFGScDYU6kUqn7/wXLINa5Ez6mrQLG4upw4BlMp6NtbrOroqypgFV2ZzKhlzGBS/Kj60dCkIi02+iqyiTcGmFm4IsBSD8U8EWOgVpNWCRtiylaJQuNDYFKWuKGF0o0OuUvu0tGsAIb5W5Xafb+kDrCf2Pf+FTHs4Ln/LwsBN+iDCmIB0IoCOllYliDdoZNTlF5n+Zur5Xx7iLZuSiJ5uKtG9U5/cQOKyFpXgjFj233UUSkL6DgXXIml39+YEpSP15olXO3VVMAJaoCkCjn8pL3z2/qpQZdln6IY9oWwLaQ0vWSivhCZNyrnXrlD6iMbz6+1KlnejJ6MInW8dPLeYagGr/ktM41gHh+qmUHlql5Ytpx6RORImOCV000WlU9ztEbKoKGq7M2shUsN6QtFWcgB2Vgsz6Wm/q/Meiee4qqea1zjBgEek7AFp1H+ICMLWpSnv9PIX4uaFTkKKx+t5wRniwkShEjIfWIMJXx4ePRZkpP7VNzHcInFvz4RxKUUfMCRk9gqSrgisQDaRIlDWNCcACWO3NMnVSkJWSFUSxcKePMQBbAuWXo7QSFXmQ/qqXAwNmtRKhk7UejLKdkRqRYwisYNfNsYfqbnS/Mb3LCnlAgKFiqVEpyPAATu3wk77S/TDjGLBMDh5a5cSfNUidKsS+2iUVMtjN/z+99DMAtVsObePz7I+9DVCBaymGYD4mhZj0tSPYjVm41fOgArDw9F2XFBRz1XfhxsQAzPRYihyLJniJuQZdUlLMaY8Cn2l9H2dVowLhSFmCWGPBUexRHZwONylIa8tixmJkGhVtQxH6PAE0FKTSdGUI18EZix8ZvaExtiqNZlNDiymc4CeSjTUbiG329TmF+4CZc1A+YOGayJoc0dVajxj2HV7wghfwyU94mK6MNgVWIfrcocBJbY7DG4qfFYwBmAPhaB1Su/CHeXD1vaSSSrxttBLBZcJmMLUz0j5ionEZsIgyX1CGhaUYFu5gBsuU7reaAQucrA0lLToVCKt2TKEMmKq+M+xRUfoHYKoiVgdgByqNGvqQX9jZUe+lPbRCmMynPfom3v19n0NWTlW6JsZQFyArEUi2OLD/DkUrch3ExbEWKpivSSNTkIZpMXrC0LE8PA8VJW2wJACgE2pTlfaRKUjNKNtq1siF34rwY4IXoRgwFYCFpyBNj9s+cizaOaCbRQfCrVCVmMoSJfwaGC0egV5oFtbHSwVQ4WxsRidSCtFE+SPCUNS1LeJ0bMIGka3WhcaNJdHNoljxb/qmb+ILnvkkclnbvqQh51A4FeKy2iWJ2ByfFYwaMAdmea8oSPsG0XXUMqcIWPg7bUJaMDAvsQ+5bFXqqgn2LRoYMFqoZUqWhukMjG+RqbQJKROGQWdQ6AAstIehCeCSLj4FqViL2u7Yy2mYbsYwYFQ6AAucaMyCY9r4xLiXm1Sw1d1EevbsCL1bjmHAREHaKeYlZqLrkoJCHEQ3ULb2A1pPGGtiqnbscYtep1OIg/FkYHeLbDKwRykRTKb5DrM19FPKmDgNTIcP82Ku2aM4Zn8orKk1axEZRMqaTBLlhXY4AIuzA7HsUVQwr57pHBHHgOlz3mFP/zvueVDfISIFiSroSLtazwlxgfR8d4yALiWOSXU/uwREpMPPCEYGzIWeaWqpK2268IV/8AFzXOijK54OyGUd0bTWYcC6iorCu5ekQW+EnpEaMCv07HQAFlDlApCmCZXMSLoaESnCV9qj2uo9ioDv4BZkoB/yPtj+wJRr6zY+oVo+hvtgLE1ig/khXRGuOWlEQdJXcW1LGPogxvqAHWLAIoOXpK+i2mLB0ENQ9DW1KIK1R5juFmvYiYDylCtERB9GoBUZeV+r9lwBGrDzGzlf9zc/hjtvOa9emF3W5xQbCCsbiig2NlELd1RXBjQTKRpEE7uhGVKQJXX4ZgD1PJU0in2KCRz0ddsSB7oZeKCIX3+HqZgpbWVkIJyYgpDA45/1rGfxzT/2axTUiC48S+QGYOjsxLXKgI0BmAOBm4I05dqZ1UD4QLUiyuZKnUMF8GaRTRq1aLaRLT8yWSPamU6jht3qXttQlML0jQsNwNQ5THQAFhrApULoFjSqd11NFsGAKe2RTZkE3AfVF1RXMVZq4Q9lHZJE9ZNUIvwm/D7iaFbayB27M1m7/w5BS07eV+Sii0xBKkY41gfMbihqoyeME+EnmnkJDmIZmMisD2OPDGRmNjRx1gEmYEvWSN81KPYolWEpSCEE3/zcJ3B+ZxsAGWstY3Sh7T6pkPEifFlTBBojG5iFOrXFQeuJ8GPGUmvGUmT6z7La7Mdpn0wK08oSYtnUWvvqRcxriPnm9iF9erNkkIdUanMc8zycBYwBmAMTZ1UosaoyQY1gwGRBIiSyUhRxbKsHoVNf4e1fTOWaav9SkwczYFJXCxkNWIhPCwyi/WmvrkGo83eifbSSrkZ0FZUsogKw3LEDCX1IrZO9Tn0RmApObBBZB1dBGvRpqQohIospDjFgkYueuY/B3k8M6eykr2nJglyzwVm4m0gNWJrRkpDJmpI6PIhFp7N1CrIVEYtmqirXNiIr19xWRLHpu1YbE2d9XCsjYzpq5qVgc2VdWJO3OnUW8x1EQdY3UeJvGAKwTEsjYhnlQrS6qXoco1yI9QOwbXEQGYBN7PHuv0OgPBbr+ApEkZDRkbXhOrbcqVAf2NgxALtuUJPrcu2GWua2F5oPTC9IGPLToVVbg4eWOj7KdFFrJURbUcmwSk5Q6bZ1NGDmO2/0pg1Q+ENeawbMdAMIuQ9gXK9V8FIHLvxuCtIsOKEdDVQlZeb074thwIxmJdK3SE/QVgMWJbjN2ZBm0YxY9AwD1jdBlaAWCwFYlkd4YKGMVAtaCPTEgyGNmkb2YbSLZmwgPFdIUEfrp3K5Tjpcp0H1ohcafJi/N7rQmEWz1fNarCWKCVjyVrN40TYSKvgIZfYBa66cRaYghbOpitI+pYYBW29TlpnCnIg5RepsU96F69iK1ElB2uxE+H04CxgDMAdWbCp1AKY1YCEb9pf9zcfadkZm4Q61cLApyFoNzqj2L9oFPqaSE4aF3zJgASamAEma08qETS0UjZmo5lPB4RqwPinIaRHdLEjz4n4+QFKrBSN0skuEKeioyGiiFj2ZquDFMmCRO/bvfO5d6t9R2qGCDcOAxWrARKMtVcKPN8F83sRV1IJZ9GrdS3K9FGRMMYUVTttAOC6VnPYVhYiwsUD31KSnkFVUOtwEPJYBCy7MUcdPOrVoxjEvKogsRVxBiFmozVgSwWyq+kzTizG0uAgGc+WsryJtKAwDth/JoBkGLH5TZtjUKBsLlA8YQNnuqvUyYHMthLCsmxmLIwN2HcBNQQ4tO8K0R1/xjEfzaU94hPqH2SkGM2AmANMCw0jPn0wa/VQEA6ZZC6NZyUNTkDqFuCkNAxZZaeMEwjEpSFApj9D0n9sX1GiPgh2nhVAMah/vGG0CYWGqtiI9sCYRO02DNinYxJigRqQg9XeIslQBMAFYG79wNyJng4pUyLgUpE4FKy3fGmkjWwwRx4AN9zGmI4G69hv9XpSA3Y6dKq4a1WgizXeI008pEX5sJah5hofq7LjnaQOliQz2pMOYK6s2PusxYAdrifjXLczJpPaJDLwPX/zFX8zTP+FvADDpd2kjNscm+LYB2DUqwh9tKBwYEb5qubFL2rcqDRaY+rJmlZYBi2sVka0RgBkTUhO8hGrAyEpSIdkQMzopyPOwh0SxP7l9yNPItFHZqUC4imDxzENatLvBC/8kH3pBGvuDGP1TbVOQbRTrgNbiGQuHWA2YoepjJ9stua8o4hj2KFVtrbK+og1sCg8D62dTV5Eu8EbzEswiMm/LElOBeNgOJDSVnNGRMOnXCKR10LUp9yIZsPlq1NAUpCmsMYU5cYa4BYWsKckiPbTMWDLPQ5w+d52ilpqcUuyT9jLShkIdsyEqmjU1ZEDcfdC+eCUNTeA4+Nqv/Vq2xEX48G9TdnvUovDuS2og0xLkUCAVE8yfBYwMmIM5BkwbFlYx1Xem6qq6rNp3BPSSBEg0TW4qdWJNF3NpTEzz4CpIE2zssB/FoAk92U5FZOAA1CIbiiFkThIZgE263WDh9Bd94l08/s5bAGw7pZgJvxYqBZnHalay0lYQxjQkH1iL+GqhVuTkQvUUDS4owbB4dTR7ZNLXudUOxenYzGYg7jsUQxVjxDU0C8Q6hrg1uS2GCDbkhbn0b1wadZjXICIFqTdlUxkfRDa6rVURaahrZARlt0cvBWkkExnrQg+mqXmr2lrFMGAu67ZOCnINBqzV5sqqqjjsGu7v77Pfqrl82oVnJwAbhCZ2czwGYNc8bAAmldty2tdUgSJ8GHQyor6igrnAwMEEK0Z0HMeA5brMV2nAQvpZAnZi2BIHwe2YDGqHWo5J+zQUpDYQDr+O5rpN+z2bfvFFkSW89DM/FhjuQ8xu1bTxKanpI8XbJQ1JV1NRhBYQDhN0rHcTCxrEiAWjTwpSISnlLGrhN+zpOgxYIwqreYlJG/U6iCxEEyd8tqxD/MJdk1st3rr3sYu5j9bCQVejBrLapirYBpEx98FIIyJSXzDMzWW3q+UlofOicaFf7z6WuiglJvhxA99sshl8PElGL4WjR4y1lmmjNJXPf/7z+YEf+QkApn14dgIGLV8SWZF7VrBWACaE+CIhxFuEEL0Q4qkLv3uFEOIdQoi3CyGe67z+PP3aO4QQ37LO5580pFT/NzYUqWxUCjJy4U/rK1Rk5IHBT5plyr2+MeLvuBRkrtmjipwy8Byks0uKEcDDfABGHkoy6951jgg/lgHbkHtRC7/ZVQ3MS1wlZ9GpiS6mdQraiNUE0qHp8CEFabQSEcGLc95yshN8vAlYttiPSn0Z/aAx9Y0tmzc7/hjrgD5VKflNqjVZh/jS/4rCVqOKiEpOl308SMPvo2E6BjuQiKIUmTPtjZ4wRgNWDn0UowJho6XbVf0Mgzc0i7Yu4ecwk6ogJOnqqOBnsjHcOzG9Kfh4dCBsx2KUpUnOhpiRiT7uedAXfir3owpz0jynI7EFUlHncAawLgP2ZuBvA7/pviiEeCLwpcCTgOcBrxRCpEKIFPi/gM8Gngh8mf7bM4FeR2A1igFL1jBdBKUdqijIA3dZiW52ahmwqHSDZsB08BIagA07vQMrRg/FPmqy2pOTaPZo6F0XEUTaAGw/KvVlenLmTaReBKhFbgOHGO1QUkzIRUfWKSYylI0dGLBL8/8OwCwZdtliciH4eHMfttmPKkQwKcipEaBHMmA7lgGLSF0lQ+l+TOCQLDJgUUaqORsyPm20n56zP8+y8ACs15+ZNbtR7c3MvGYKc2LuQydySqFTwZHpcFDFDFHPU5ojEeysoQGrZE4pmugU5M6FW62NA9Pw5xGUNnWdNGqXlMOGJoLJNOe/0cfpEYs0UVW9pj1ZxPNwFrBWACalfKuU8u1LfvV5wE9LKSsp5V8B7wCerv97h5TyXVLKGvhp/bdnAi4DlkvVsqNmDbO/ZpdaZuRZ2EOu/KPyoeorqv9eqdOolWqcGjrR5MNOzxiShuIiyjn7ktiKOr4VBUV/QELPTTtblIFaOsMcbso4nUGSJMxkbgW7wZ5BqDSqDcAiJhpjYVJ0e1E6uEMi/IixtJ8NC7fYCN9xu82DY9jcrFDs6VTu0UQYuYIaS0Z/FZuCBBVAxRzvPk9AdDC/qRmwmEKC3Wy9AEw4FYQV4cbIifbFm2oz2pjCHBPAb3EQpYMzm+NpvxdVWY0QdEmx1n18wl23allBpA1FkiJM4BUZgFUUa6VRO1EoOxQi03+GASNOllBmicqQGJPsiGD8LOCh0oA9Aniv8+/36deOen0phBD/SAjxRiHEG++7776H5ERd9HMBWENCTyPC2R+zWy7aK4rFCVwwbAC2TgpSayXSSNdr81DtiP1oBuwiapK/TFwA1oicUrMejzB96AJgdrsFbdQ1MJWceR/pXs78d4gKwLT9R9ntRbVjskzLGhqwvey8/TnZOH/k3x0FlwGL0U8ZE98EqQKwCLSaOVFvGM+cbLMfxdwIy4CtI8Iv2Fpj4XcZsCo/d8xfHoF0MFKNaW8GzG1ogzsaMKTxEyGjiinMBmQiD6hkHtzSE1Qwvh1bzQo86rYL3FxKZS0Ta59gNlYnwYBF6joNohhl58LHZCfyNKGmUCwi1zEDJoR4gxDizUv+e8iZKynlj0spnyqlfOqtt976UH8c0qQgHcYnzqNkGAwVOUWggD3R/lG51g6FNoEGRdXnUrVOifP8Ud9hi4PBdTgQDwodgEUyYI0obNn9ue3t4OPdypioFKROmVjEVEGitHgQl0o2+ie7Y19XAxYxlg6cACwvw1kLqwETs6jPL7QmEqCKaQPEQj/VNWwkUiGjmBvDmu2ssejV5MoOhDgGzGUyowIwowGTccbIMK8Lzabhz7Tb/zGmsMcdfzHdNQD6JF+rjyJZCVa7FGkgasRrsQyYzEmFHM4nEG4RR6gs4cUvfjGf9MzPsv9uI1zsC82A2XOI0ESeBazcTkopnxPxvu8H7nL+fad+jWNeP3Xo4TgXcOwn4eJx0uH4GA8uw4AZRJWcJ4Wt5IzR3bg79joyBXlZ7ICEmshFU+Sk9EAcC+iyLVUaHgQaBgyglUnUoteIfBhYEd/BGOBuyj3uYzt8x57mgIB2pv6fhDNILgMWXE3Lgn9axDUo84SKgoIDrohtYpacuRR0RCGBuwkK7QoBQ8XfOhqwWuRK9Ewc6+BuAOo8IgXppF4rmTMN9RYEZi4Dtnlz8PHuRkpsxOsR1bmUUUHkHAMWFQS6z0MkA5asF4DNbSxj5rV02ISIQFb8xS9+MRe3fx3+XFVCziIKQopsaEfUS4Eo4jb5p42HKgX5C8CXCiFKIcTdwOOAPwD+EHicEOJuIUSBEur/wkN0DsHodA7ywAkYdiPYG5kNg/OS3Ay2cDCGhQZ9pFaioCGTdZT426X3axmZgtQMWMQcBygGzCJml5YP4vGYqq9ECPal+tyLbJFEaY+G+9jk4Tv+rDQB2L5y1Y/QrNgJP5sEtfwwcBmwUDYXmA82IsZykSZc1laNu0n4NQQ4cAPwiEXLXbjTCB2c0Q+qQoQ8SsfWOMGLKMLtB1y2p4kIwNzA4SAyeLmkdaGtTCim4edQudq1iApAUQwb6gflVrg2FjUW1umjODeXlXHjeV0GzA2EKcPvw0E+fG4S+Dx89KMf5YEDaf99EMHG5mnCPmouucRmuD/iGcG6NhQvEkK8D/gU4L8KIX4FQEr5FuA1wF8Avwx8nZSyk1K2wMuAXwHeCrxG/+2ZgKmCfFAOk/WuCH9A6mIYUJfYIg9lwFJhg8AOQR+xcHdJyYSKlD6qW7274OwG+xQrXDYBGHLFXy5H46SbuogFoy2HiWEWcXwiBA+aQgK5GbXg7IlhoawjJhojwt9iPy4FCcOEP4lIOwEH+Xn7cxQD5liQ9OX5o//wCJR5ygNS3Ye9JG6nu5c6nxuxaPXOM51vhS/8Jm25zUHU8wjMp1ym58PPwRm/7ibRFyIrbSr4QbkVpQGzsgQ2gu15AGZzC39E8JFv2KKiy0R4aKHmxk2hWoOtzYBF3EcAhA44Ip/pJz7mkeqHfMMWiITADcDSzbD78IVf+IX8h3/1Cho9lmLY2CJLbJHXxci5+SxgrVZEUsqfA37uiN99N/DdS17/JeCX1vnchwomTHhADgPiSsSOO8kKLssNdsQ+FyMZsIs6CLwst0gDy70BuiRnQ1cbRdlYOFT/JRk3URl/l1SnTUKx51z7bnI+/POL4T7G0NxCwP16LFxkixjy54oYPjcmADOTdSla5QMWM9FYwe758GOBLhsCqBgGrHM0IjLCxqJIE+7T92E/iWBumNc/xez4m8kQdOVb4akz299VSNrItNOMYaGMCT7csRO6KQRI04QH2eZ2LurnIfw9TGFOG1NQAuw7m4FQ5gVAJAkPsM3DeIDLxLFPc4UkMRWA7viLmNcAuOVx8MA7IYIJVaegNdWxGrLivP05JpUsgJ4E6KL0iGWa8IAeS5fYYnojBmDXG4wI/37nwYzZcScCrjBlh30uyq1gDViSwP16x3+RTbKIRa93zTNjPIey4XtfitwpXtbs4YeSO6KOv+IETV0ZPlG4LtdVLAOm78ODcoubI9inK07A0BbxwmdQPlBR2LgJdj8UPdkLfR1/vbuXp0ewFp0TvBAROJR5wv16st2LTEHOBWAR99H9DjELf+qwDFFNpIGLyQ500MiUpIxJQcJ3FV/PzsH7ooKfVAgekNvcLi5yUW4Gd6aAgQFL6aPSf5UTgGURTGQiFGPyMPEAl0QsA+ZKIyIYsM1bhp9jGbAX/Rt412/ATXfHHb+hzyE6ABuufbYRt7ntTe/lyBSkmZsvyi22xgDs2keviRpzYwF2RdzC3csEhGJOQo1Y04XUV8xE51ap9BHpBneHE8uAvT17PF+99w186Nyn8sKI43edsvk+Inhwr1uVhT/kSQIP6PtwmU1ui9G8JMPnNkVE8OBM8HsiLhVsJ/zIyTYVgsfNfpKOhLfFMGBO2jHGR6xIEy5L9d33s9gA7PzKvzkOj33UncM/IhbNJEmtAWcXE4gDF4X6XFXYE86KJ0Lw+uRZvL854B/HBGCp4KLdGG5HsbGmMMcU14SiduQYMcxLIgQd6tpdiWXAHG1p1DM1F4DFPZNML8CTXhR3LKhNGUR1KAGoiuG88zQmjBA2AGsinociS/ioHotRfm5nBGMvSAdSJyEfdB7MWUQVpHrI1aW9LMInqjQRNg3akQQzaABNOpx3DMUrHf+zWAYsEYJf6Z9Om4YHgAB7TvASk4J0r3sMA5Y6DJhEhLcBYp4Bi1k0XY1H7H1g06QbzkcdXmYJDRk9SVTqKssG5i5GwF5mCa1eNKPaOTGvY4vBnTetJ+JPEnhQ++H1EWlYgEtCjYWcLmrBSRJBq3eZwT0QUc/DTLOwVyK0sQAX9XdItYlnKIRz3sVmxKZKDAv/lUh7nF6L/2cUUEQEMOZ5hPgU5LrYMMFrnD7XTb3GBOJDChLaiOxGkSV8tFP3L6GPIinOAsYAzEGnN2W9c1mSiEUzTYQd1jEPuXAYsJQ+qhH2zAm66ogdxk2bBb1Ug/qrP+sTgo+HQfQb+3DsOaxVGiEUdS9bXcTQ5IIrutJGIKMWvV1H/B21S9sYdvmxmhWbboic7DcK9QzkqYhKG53bcLyfItJGZZbaDU0WyZysG4DNISIAc4P5mGsAA5ua0kU9U6kQ1K26fjGbuiwRdm6MDV4urVmY4xah5BFzs0p9Jfpc4r6D1JuIWINp+zxClB7xRGDmFRn3PK3DOL30pS/l0z/v79h2RG0Zl4J8oFcb0gQZp409AxgDMAdGA+Yi5r66D/l+pGbFiFUFMmqynTkpxMb52Rcf+7Ad0NTyXQ9/ePDxMFy72IfDTUHGsE/uLj9GZ5AI6KR6j4Q+LgBzGbCY6+AETbGLng0YIiwgAKaFGgdLHg8v3LY9BM/59i3H/OVyFFlinydrHhmI6iQDsIjKs0TrpwDK7fDUGQwMWCbixmKSOAFY5PGGPdqL1E8ZBuyD4vao4915IGZecRmwWWRKX07V/ZtFtKkD5lmzCCbyRGCY6NgATN+HGHnKl3zJl/BJz34B75G3qVOICMCKLOGg13PCyIBdH1g2tUeJVRNho/sYE1QYFtuUPkqE75ZrdxGl/wCJMZSNTF2ZyTL24XA7CsSKhv9T+5kAtBFWHokQXNS73I/IC1FBoCvYjdo1OvqKyxF6RPUe+j72bdThhgFr+7jg5+bN4RoUEexPkSW8uv0s/qR/LL9//vlR51DqjgJ/mD8t6vg5RHgOJcnAasfo4AAupcMzHcWAJVB3JgUZx4DZJtCRgUOVTPnf6pfxium3Rx2fCPg/my/mp+Rnxx2fwP1SLfhdRJs5wLJH1+aSr2F0wZG7qjQVPGn273m2fGXwse9973u5dN8HeUn9zby8/pqo9SlPBO+TKpX7xv7xUXPzWcAownfQO4PxG87/ELdupCT7cbus/715Kd+x/fN8oD+y1eWxqBIVfNwvd6J0N/UJBGCY/omRqSszycfS1e5xUf6fCXxr+xK+t/07vCTCAT4Rgl/vP55/d+Eb+f4PPoW/FbXjVv9/n7wlSnfj4kqkB9YQgMXpbqbFeiaHLlMx3Yio3ksEH01u4UX1v+SFRVxLsmmR8eTZv+Xxt9zGa6PeAfib3wp/9T9ij+ZA6kA0Unh92WFTYzckTafmuFgG7C/ko/gs/oiLSXxBx+v6Z/D4PG4sJ4ngh7rP51yR8+WRn/9Pm3/IH/eP4390j4o6BzZNABZJCZ8FmOrJT/qaqMPLLGWPKU0fPqd9+Zd/OR+6NGP23H/Bz/afwSdEVFanqeB/ybv4mae/lv/rN2d85TXKgI0BmAN3g/+O9DFcKSaks1nw+yRC8GfyY/jWzf8DcdBEnctfiUfymlv/Mf+f934sL49YuOd8WsrI6rnpOaguRbs1DxqwuMAjEYK/6B/Fu+QdPCIyBdmTcIWNqAVLfaTg16bPo+L+KBPUJBF8wearefv9Dd+25iQRK3zmpsfo/8eVrG+sGYABfEH17dybvJNXREy2oIX4dUceeQ03ipTLbM6ZmQbjmd+s/ovEI2/ZgotE634OtB7xld3n87URY9HV76URO5osEfxw+yLeUt7LW9OPDT7ePYcYXSusz6oLIXiAHX60+9yo4wF6bUkSq2MD4G//W+ji1oYTwcZN8B2Xog+/oHWdJqUdDOf2hboEuMd8sHgUkr8kjSApzgLGAMyBqwHre+hknPDaHFK1XRR7BZAkCb+69Xncz4ejJhvX6bqMXPT4u6+FP/sZ2Lot6nDjmh6rAUsTwfPr7wXgFyJTLu57hcKct6kci4kjUyH4UL/DLgdrC0VjujIA8IQXwFe8Dh71aVGHbxbrTxN/JO/hj7p7+BeRC2+RJezVcdV/MASR0QvGCeCTHnML/DFRPmSgKgAfPftPlFnC10Yc7167mEDWWDj8fvcEdvLYeU1/fuQ4WJdVPwmiRGgGLEvWCMCe/MXrn8gp4sJmpP5Nw70NUQUh+phKP89RHULOAEYNmAMTfwmh0pFdLyPF38PgiNFvmfeoWpUyihmg7seWWSSDces98Oxvi14wjGt67G7VnWRj7sOcYDfqePX/WqdtYoO4posXPruI7YOIEHD3Z0TrdtZNQQJsrvkeZgzHPAsAGzqIrNu4NOzJQJ97pO7GDJ+TeJ6iNGD62tdtH30fzHMY09IKhqko9hqchFbolptVGvzCbXet/V7XKm7aWDMAm6tmjWNjAQ5q9TzHjqfTxsiAOTAasDxJkFL9O44BGwKw7UlcyiMRwkb3MUGcy7ZEM2BrwjwU0Row5yGNK5sfvndMHGzuY9vF77ISgdXdxF6Ht537dJ5w6bdokgjX7RPASaQgf+3lz+L9F/ejj193LFkGrDs9BoxPeRn81W8qRjICZvjFsz/rPU+pM6+t+0xHNXVn+A6xKacTEWvvPBxe8IPkj3/e+u91jeKmE2TAorwF9fi5MmtJExHNqJ42xgDMgQnAslTw9g9f4cNXch5/W0T1XDLsFGNTkGky0Ksx6YI8Sfii6ttoSfnC/JQCsBNkwOL0V87PUZoZ9f9WB1AxKcTUKf2PXbT+y+O/hy/7rbewvXE6NPtJBGB3nJtwx7n4ANIEYLF6wukZSEFy6+Ph698UfbjQy1Y8q+7+HCF8dsZvbABlnqnoeXFNDdmJZaqe+g9O6I2uTayTgnz5y1/O77zjo/z0h9W/Y+6lGYt7VXtqBMNJ4No984cARoRvbu7F/SYqazOvAYu7xFmSUDUmBRn+HtMi5Q/lE/gT+bgT0fDEYF3Wwg14ooIfd8e/BpPZ9OswYIKZvo+xi1ZRTHiQnVNrtzE9pfHjojwpBuw0A7A1cZIMWMymzv3cdVOQ6wZQFyJTYNeqYedZwzqSghe+8IV88rM+y/47Zn0zAfxe3TLJ198gnhZOf2Y9QzAifHdyiC33Bq0Biw4+WMu1eqscbu1meW0GYO7Xjgl+3Ou2Tip5XQbM+GflkTs1M8Gc1tqxcQYmOMuARWvAroMATP//JPRPsf6GButWMcY+C+YcogOwMf46EcR0xDB4+9vfzgfe82H77xg21DC4u1XL5BpmwMYAzIFcYMAgNnUl7PvFigNTVwMWMWu4Qde6AuhYGMbnJBiwdQKoxffyRbqoAVvzHGLTLob9OS2zwZMQ4a+LMlsvnT3N1fNg9HjXIsy8chK+eutqKtdlsKJTmPr/N23Ga2tHnC6++qu/mvv3anj2PwfixpJhcHdnIwN23WAQ4Z/cTjGeARNUVavfI3yAukHXaTNg0RqwdQOoNTVkQl/2RjNYsSlIg9hFx0wwp5WCPAsaiyIz1yDuXDbLMyDCXxPrVgDOV0HGFPYMP8eOZXMOsZuRfZ3Oj2fAxgDspPBjX/6J0cGPexdixrNJW+5VLefWrMg8TYwBmAOjAXNz0rGVbwbRfjdCUDVahB8xWW2eqRTkepM1xN2HOSZzrRSkug8xc7d7+2PHwiQ/XQZsnXTDSWFtBuwMsHjrwnzz09KAuRvB09KAPbhXA/EicPdr/+xLPyXqPUYoPPdJd0QfK9ZcI808sFu13LZzOtXhJ4HT39qeIRgNWLbuwn0CWgnlAxZvQzEfgJ1SCnJdBuwEmch1Ammj4Vr3HGLT0afNgBl87MMie1GeANa3obj295omEI6tBF3XmDgV689r5mNjj39gT7nHx/pQmQBwmqd84qPienKOWB/u6IsKwNIhAJucUpX/SeDan5VOEAMDtu7Cvb7uJxHCpkviNGCnn4Is19SArRuArVtMYasg1/ABW9dwEBwN2CkGYH/0rc85VRbJjKXY52l6DetEDMztPzUfMOeYeBsK9R6xm5EH99djwMxm6iSsVUasg/XWSPMM9JJrWgN27YaODwEkhgE7mSpIWMezZ72F2w26TquKzUyyfbTz93qBsLtIxAQv5iNNNeu6Vhhra8BOkQC7eas8VRapzE8unX2tYvABO4kqyAjvpRPYWK6rAXvxMx4NwCc+6kLU8TvTDCHgnz0/rpfliPXxrd/6rXzF136j/XeUCN85ZhLb6eUMYGTAHGi7p7kJbp2FG9afqBbPxxdu0BUbBK4LE4B1fVwANq/hCj8+X1vLp46Rcv2yeYA8ixsLp60BOwtY19QX4HOf8nD+1hNvP6lTuvpYkwFbtzhofk5ab06JZYM/4/G38u7v+5zozy2zlL/63vjjR6yP5zznOWy886Pwtv8JrKcBg2Fzdi1iDMAc9Es0YDKCvTkJv5xszcnytIIuF2bRjGXATqp3nTo+/PNPKpU8vEdsClL7gF0HLE4syhPQwf3Ql338SZ3OqcB883Uqqw1iNnXuMxA7li8fKA1XbIu2Edc+3vSmN/HO9120/17XEmVkwK4zuDc3JniY01qcQMrkLARTMTDWAbEM2LrNtN1FYp1m3DB8l3XeY90qyBgW73rBSTBg1zpsH8ST6K0axewLiiyhbnuKyA3J/bqK8fadMur4Edc+vuEbvoErsxae9QpAtc0LhRu0Xcsi/Gv3zB8CdEuq3doI48Y564HItJO7WMeUjJ8FmIekjU5Buj+vpwGLOV7M6bfWT/vEasDK7GxUQZ4m1m1FdD1g8AE7nV6QgHUdj90UPqADsNu2r13rgBHrQ8xtbtdLQY4i/OsEbjNugxj2Zq7y7QZmwMyOu48OwNbTcK2r5QOnbD5SAzZvSRKrARtTkGaSjmURrwfYACxyHIk1GTAYxuK69+G27ZEBG6EQU13trolnwSg6FmMK0oEJE9ysYwx7cxJ+Oe5iHTvhfuzDdtg6JQ8wGILI2O4v6/qxucfHpu8SIeilXMtQF9TiGcveDCL8qMOvC6zrA3Y9wFZBnkQKMrYtlh6LsYywwW1jCvKGxrpP8bwI/9plwMYAzIEJvFzdVwwDtq7fDiwI+SNZtNd//adHHXdSMEFTrAh/3V22mCu7X0dEHx+AmY8t0iTaUd6mIG9gDZi5BjeyBszc/tPy1YNhLlr32bwejHFHxGPd7hrzGrAxALsuYAKFtQOwE+iZ5lKssUHcaWPdFGSsWeMyxFo4JAnQxe/4TRAaOw5AsaGJGFOQcIMzYGs64c9VQa75HteqLGLE6eN7vud7eM8D+3zLb1dsT+JCkLkqyGtYhD8GYA4GBmx47bQYsHVtKM4CzGIZK8I/yQBsXffwdVOQsRoyUAvvJE9vcAbMiL9v3Gtgvnl0KtsZg+tWUsbqGf/DV34Sl2dN1LEjrg884xnP4MIHL8Nv/xa3R/ZxnEtBjjYU1wdssDWnAeuD3+dkfMCGHf9ZaIYcA9suIjIAK09wlx37VmsHYOl6C5bBJE9vaPZn6Ct67e5218VQBRk3DtzuGOt6icVujj7tcbdEHTfi+sHv/u7v8p4H9gH41I+5Oeo93I3Y5jXcVmoMwBzIk0pBnoCBp5kgr1X2C4agp4vUgJ2FFKQ5LJbB2johrUuZJTd0CrJcs7H79YB1GbC5ACxam6r+fyNXo45YD//sn/0zAH76J342uqWUuxE7rV7HJ4Fr98wfApgwwQ3AYtJnbiPs9ZmTa3eiM0FPrBHrWUpBxmrAzm0ox+/9uos63uDRN2/yiPPTtd7jWsadFzbIEsHDbuBrYMZibPDkMgXRXmLi2t8Yjjgb+OTHxLFfsMCAnWKl/7oYAzAHJvByw4WY4GF6An0Y82S9yfYs4Ml3ngfgKz/t7qjj1xGuLyJahG8YsMhzOTdVAdisWS8A+49f9Ulco5noE8Fjb9vibd/5vBta/L1uFaTLFMTGT+umIEeMOAm4G4CRAbtOYGItV7MUw4CdjIP6ta95uWmzWKtx7lliwGIDsB0dgDWxZmjmPEbG4YYOvhTWq4LcdNLhsbrSdM3nYcSIk4A7fjevYUuTtZ4iIcQXCSHeIoTohRBPdV5/tBDiQAjxJv3fv3F+94lCiD8XQrxDCPFD4gwpzG/eLNQPzinFps8MYidLox27kan+sxCAmeEZey47Y9PhESeEdRmwjRNI1ZiNwBiAjTgruJEZsDcDfxv4sSW/e6eU8t4lr/8o8A+B/wn8EvA84PVrnseJ4FX/4On8xts/wuvf/CH7WkwVpItY8XZ6HaQg18VZEOGfVApyxIh1YcZi7KbsJIKmbJyXRqyJH/zBHzzR99u4hqsg13oipZRvlVK+3ffvhRAPA3aklL8vVcnhTwKfv845nCTuODfhS5/+yDnWq1szdRTbSNukW27kneZJasCiy+7XFeGPAdiIE0Kn94KnOSeYjWHshmbEiHvvvZd77733xN7vWu4F+VCe+d1CiD8RQvwPIYTpifMI4H3O37xPv7YUQoh/JIR4oxDijffdd99DeKrzcAOwFzzl4Wu9VywDZgKGG3meOwspyJEBG3FW8K6P7gKqIOG0YFLyse3FRox4wxvewBve8IYTe78zpGIKxsoUpBDiDcAdS371z6WU/+WIwz4IPFJKeb8Q4hOBnxdCPCn05KSUPw78OMBTn/rUq/bEmwDs3/79p/KZT7gt6j0SoUT9sczLSPGfcBXkmhqw2EB6rBYbcVJ41317APyNR+yc2jmYaSnWXHnEiO/6ru8C4DnPec4pn8npY2UAJqUMvkpSygqo9M9/JIR4J/B44P3Anc6f3qlfO1MwxqEXNvJo5qTIEmZNv4YTvmbAoo6+PnCiDNg6vSC5sVPBI84W7r7l9BgwMx+uW5w0YsSIh8iGQghxK/CAlLITQjwGeBzwLinlA0KIy0KIT0aJ8P8+8MMPxTmsA2M9sU7pf5GuG4CNC/6JivAj38rUYMRqwAC++bn38JhbNqOPHzEC4Ef+zsfzV/ftnWpLqi1dcXYtp31GjDgrWCsAE0K8CBVA3Qr8VyHEm6SUzwU+A/iXQogG6IGvkVI+oA/7WuBVwBRV/XgmKiBddHrVXccCoshSoI1vxj2mIM8EAzbVFTbrMGBf9zcfG33siBEGL3jyenrUk8B3fO6TuOumjWhpxogRIwasFYBJKX8O+Lklr/8s8LNHHPNG4G+s87kPNUy10To7TVOZkUdSL4YBu5F3miepAYu9l7dsFbzjI2MKcsT1gV/5hs/ggb06+vjzGwUv/6x7TvCMRoy4cXHtOpg9hPj0x93CWz94mVu2yuj3MOxNE+kjdiMbsBqcBRH+zXoMxIrwR4w4S7jnju3TPoURNzh+7MeW2YaG49de/sy5tn/XIsZVZQn+yXPv4Xe+5TO5fWcS/R5/5+mPBBx3/UCYFOSNHIadZPud2BTkLfr+VWv2chwxYsSIEXDPPfdwzz3rs6gfc+sWDz8/PYEzOj2MDNgSZGnCI9a8sV/16XfzDz710dH9605TaHs9Ij4FqRiw+9dI24wYMWLECIXXve51ALzwhS885TM5fYwB2EMEIcRaQnqjObqBJWAniljn7tt2VAC2O2tP8nRGjBgx4obE93//9wNjAAZjAHZmMTJgJ4Pbtks+cqWKvp6fd+8j+LP3XeIfP/txJ3xmI0aMGDHiRsYYgJ1R5KMNxYngZ1/6DH7vXfdHB2CTPOW7X/RxJ3xWI0aMGDHiRscYgJ1RpKMR64ngrps2uOumjdM+jREjRowYMWIO4yp/RpHbVkQjE/awc/HVqCNGjBgxYsRZxMiAnVGMGjCFv/iXz40W0I8YMWLEiLOFn/qpnzrtUzgzGAOwM4psrIIEYKMYh+iIESNGXC+46667TvsUzgzGFOQZxeiEP2LEiBEjrjf8zM/8DD/zMz9z2qdxJjDSC2cUYzPuESNGjBhxveFHf/RHAfiSL/mSUz6T08fIgJ1RjLqnESNGjBgx4vrFGICNGDFixIgRI0ZcZYwB2BmHGJmwESNGjBgx4rrDGICdUUh52mcwYsSIESNGjHioMIrwzygM8TVWQ44YMWLEiOsFr33ta0/7FM4MxgDsjOKe27d5yafezVc841GnfSojRowYMWLEieCWW2457VM4MxgDsDOKJBF82wufeNqnMWLEiBEjRpwYXvWqVwHw4he/+FTP4yxg1ICNGDFixIgRI64KXvWqV9kg7EbHGICNGDFixIgRI0ZcZYwB2IgRI0aMGDFixFXGGICNGDFixIgRI0ZcZYwB2IgRI0aMGDFixFXGWAU5YsSIESNGjLgq+KVf+qXTPoUzgzEAGzFixIgRI0ZcFWxsbJz2KZwZjCnIESNGjBgxYsRVwStf+Upe+cpXnvZpnAmMAdiIESNGjBgx4qrgNa95Da95zWtO+zTOBMYAbMSIESNGjBgx4ipjDMBGjBgxYsSIESOuMsYAbMSIESNGjBgx4ipjDMBGjBgxYsSIESOuMoSU8rTPwQtCiPuAv34IP+IW4KMP4ftfKxivw3gNYLwGMF4Dg/E6jNcAro9rcLW/w6OklLce9ctrJgB7qCGEeKOU8qmnfR6njfE6jNcAxmsA4zUwGK/DeA3g+rgGZ+07jCnIESNGjBgxYsSIq4wxABsxYsSIESNGjLjKGAOwAT9+2idwRjBeh/EawHgNYLwGBuN1GK8BXB/X4Ex9h1EDNmLEiBEjRowYcZUxMmAjRowYMWLEiBFXG1LKM/kf8BPAR4A3O689Bfg94M+B1wE7+vW/C7zJ+a8H7tW/+0T99+8AfgjN+i35vOcBb9d/9y3O6y/Tr0nglmPO927gf+q//Rmg0K9/BvDHQAt84Q18HV4M3Oec21fdgNfgUcCvAX8G/AZw53V8DZb+HfB5+vu/CXgj8Gk34DX4Zue83gx0wE3X8XX4j/r4N+tzz/XrT9DnXAHf5Pv9r7Nr8CzgknNu33YjXQP9//uAy6h54Q+Av30K32Hp/Vly/Imu894D/mr/p7/QJywMrj8Enql/fgnwnUuO+zjgnc6//wD4ZEAArwc+e8kxKfBO4DFAAfwp8ET9u48HHg28e8Xgeg3wpfrnfwO8VP/8aODJwE+G3Jjr8Dq8GPiRG3ws/GfgK/TPnwn81HV8DZb+HbDFIH14MvC2G+0aLPzNC4Ffv86fh+frzxDA/83wPNwGPA34bsIDsOvlGjwL+MWQ7349XQP9HV4NfFj/zRNQwdjV/g5L78+S9zjRdf7MpiCllL8JPLDw8uOB39Q//zfgC5Yc+mXATwMIIR6Gip5/X6qr9JPA5y855unAO6SU75JS1vr4z9Pn8SdSyncfd65CCIFaUF+rX3q1+Rwp5bullH+GitaDcb1ch3VwHV2DJwK/rn/+7+Z9fXAtXYPj/k5Kuas/G2ATtWv2wvVyDZac2/+96r0W3vdauw6/JDVQC+Wd+vWPSCn/EGhWvceS97wursE6uB6ugf4OdwF7+m/ehpoX3naVv8PK+/NQrPNnNgA7Am9hWLS+CHXjFvElDBPaI4D3Ob97n35tEY8A3uvxd0fhZuCilLKNPD4U1+p1+AIhxJ8JIV4rhFh2ziG4Fq/Bn6LodYAXAdtCiJsD3nsRZ/UaHAshxIuEEG8D/itqh7sOrslrACCE2EClRH72BN7uzF8HIUQOfDnwyzHHe+BavQafIoT4UyHE64UQT4p5XwfX4jV4K7Cjf/d0VFzy9/Tvrup3WDFGT3ydv9YCsJcAXyuE+CNgG6jdXwohPgnYl1K++TRO7iriWrwOrwMeLaV8MmpX8+o13+9avAbfBDxTCPEnwDOB96P0P7G4Fq8BUsqfk1I+AbV7/M413+6avAYaLwR+R0q5yGLE4Fq4Dq8EflNK+VsP0ftfi9fgj1Htap4C/DDw82u+/7V4DX4USIUQbwL+N5QO6wtP6Ts81GN0DtnV+JCTgqYnPwtACPF44HMW/uRLmafz3888lXgn8H7NvrxOv/ZvUMzEXYt/d9y5CCF+BbgdJST+h8B5IUSmo+OVx6+Da/E6SCnvdw77d8C/Ov5bHo9r9Bp8AM2ACSG2gC+QUl70+LpLcVavgZTyqzzP/zeFEI8RQtwipYzqz3aNX4PFc4vGWb8OQohvB24Fvtr/W4XhWrwGUsrLzs+/JIR45fX8PBwxDnaB90sp79Vpvr8CPlVKeflqfodl5/aQr/MyQvx3tf5DCdtcgeFt+v8JKs/7Eud3ib4Yj1l4j0Vx3vOXfE4GvAtV4WDEeU9a+Jt3c7zA8D8zL8772oXfv4oIEf71ch2Ahzl/8yLg92/Aa3ALkOifvxv4l9frNTjq74DHMojwP0Gf49KKpev1GujXzqH0O5shY+BavA7AVwG/C0yP+P13ECjCv16uAXCH8zw8HXjP9fo8HHMNngy8Rf/8D4HXXO3vsGqMOu9xout88IN/tf5DRbgfRAk03wd8JfD1wP/S/32fO1BR1SSHFnXgqShK853Ajxw1uFFVEP9L/90/d17/x/rzW+ADwL874vjH6EHwDn2TSv360/Txe8D9ZqDdgNfhe1H6hD9FCdCfcANegy8E/lK/978zr1+n12Dp3wH/VI+DN6FKzUNsKK6La6B/92Lgp0Pmgmv4OrT62DfhWC2ggo/3oareLuqfd26wa/Ayhnnx94Fn3EjjQH+Hj6KKcRrgj4BvOYXvsPT+LDn+RNf50Ql/xIgRI0aMGDHiKuNaE+GPGDFixIgRI0Zc8xgDsBEjRowYMWLEiKuMMQAbMWLEiBEjRoy4yhgDsBEjRowYMWLEiKuMMQAbMWLEiBEjRoy4yhgDsBEjRowYMWLEiKuMMQAbMWLEiBEjRoy4yhgDsBEjRowYMWLEiKuM/xcMnpwj6/Yc4AAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Difference transform + invert...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "21:14:30 - cmdstanpy - INFO - Chain [1] start processing\n", + "21:14:30 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train sMAPE: 6.47\n", + "Test sMAPE: 19.18\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from merlion.transform.moving_average import DifferenceTransform\n", + "\n", + "print(\"Difference transform...\")\n", + "eval_model(get_model(DifferenceTransform()), train, test, apply_inverse=False)\n", + "\n", + "print(\"Difference transform + invert...\")\n", + "diff = eval_model(get_model(DifferenceTransform()), train, test, apply_inverse=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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A Gentle Introduction to Anomaly Detection in Merlion

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We begin by importing Merlion’s TimeSeries class and the data loader for the Numenta Anomaly Benchmark NAB. We can then divide a specific time series from this dataset into training and testing splits.

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[1]:
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from merlion.utils import TimeSeries
+from ts_datasets.anomaly import NAB
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+time_series, metadata = NAB(subset="realKnownCause")[3]
+train_data = TimeSeries.from_pd(time_series[metadata.trainval])
+test_data = TimeSeries.from_pd(time_series[~metadata.trainval])
+test_labels = TimeSeries.from_pd(metadata.anomaly[~metadata.trainval])
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+Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/ec2_request_latency_system_failure.csv (index 2) has timestamp duplicates. Kept first values.
+Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/machine_temperature_system_failure.csv (index 3) has timestamp duplicates. Kept first values.
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We can then initialize and train Merlion’s DefaultDetector, which is an anomaly detection model that balances performance with efficiency. We also obtain its predictions on the test split.

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from merlion.models.defaults import DefaultDetectorConfig, DefaultDetector
+model = DefaultDetector(DefaultDetectorConfig())
+model.train(train_data=train_data)
+test_pred = model.get_anomaly_label(time_series=test_data)
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+Inferred granularity <5 * Minutes>
+Inferred granularity <5 * Minutes>
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Next, we visualize the model’s predictions.

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from merlion.plot import plot_anoms
+import matplotlib.pyplot as plt
+fig, ax = model.plot_anomaly(time_series=test_data)
+plot_anoms(ax=ax, anomaly_labels=test_labels)
+plt.show()
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+../../_images/tutorials_anomaly_0_AnomalyIntro_6_0.png +
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Finally, we can quantitatively evaluate the model. The precision and recall come from the fact that the model fired 3 alarms, with 2 true positives, 1 false negative, and 1 false positive. We also evaluate the mean time the model took to detect each anomaly that it correctly detected.

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from merlion.evaluate.anomaly import TSADMetric
+p = TSADMetric.Precision.value(ground_truth=test_labels, predict=test_pred)
+r = TSADMetric.Recall.value(ground_truth=test_labels, predict=test_pred)
+f1 = TSADMetric.F1.value(ground_truth=test_labels, predict=test_pred)
+mttd = TSADMetric.MeanTimeToDetect.value(ground_truth=test_labels, predict=test_pred)
+print(f"Precision: {p:.4f}, Recall: {r:.4f}, F1: {f1:.4f}\n"
+      f"Mean Time To Detect: {mttd}")
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+Precision: 0.6667, Recall: 0.6667, F1: 0.6667
+Mean Time To Detect: 1 days 10:25:00
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/anomaly/0_AnomalyIntro.ipynb b/v2.0.2/tutorials/anomaly/0_AnomalyIntro.ipynb new file mode 100644 index 000000000..725e51162 --- /dev/null +++ b/v2.0.2/tutorials/anomaly/0_AnomalyIntro.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "35f92df6", + "metadata": {}, + "source": [ + "# A Gentle Introduction to Anomaly Detection in Merlion" + ] + }, + { + "cell_type": "markdown", + "id": "6504e0e6", + "metadata": {}, + "source": [ + "We begin by importing Merlion's `TimeSeries` class and the data loader for the Numenta Anomaly Benchmark `NAB`. We can then divide a specific time series from this dataset into training and testing splits." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "caa231be", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/ec2_request_latency_system_failure.csv (index 2) has timestamp duplicates. Kept first values.\n", + "Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/machine_temperature_system_failure.csv (index 3) has timestamp duplicates. Kept first values.\n" + ] + } + ], + "source": [ + "from merlion.utils import TimeSeries\n", + "from ts_datasets.anomaly import NAB\n", + "\n", + "time_series, metadata = NAB(subset=\"realKnownCause\")[3]\n", + "train_data = TimeSeries.from_pd(time_series[metadata.trainval])\n", + "test_data = TimeSeries.from_pd(time_series[~metadata.trainval])\n", + "test_labels = TimeSeries.from_pd(metadata.anomaly[~metadata.trainval])" + ] + }, + { + "cell_type": "markdown", + "id": "1ab7488e", + "metadata": {}, + "source": [ + "We can then initialize and train Merlion's `DefaultDetector`, which is an anomaly detection model that balances performance with efficiency. We also obtain its predictions on the test split." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "26f41bf4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Inferred granularity <5 * Minutes>\n", + "Inferred granularity <5 * Minutes>\n" + ] + } + ], + "source": [ + "from merlion.models.defaults import DefaultDetectorConfig, DefaultDetector\n", + "model = DefaultDetector(DefaultDetectorConfig())\n", + "model.train(train_data=train_data)\n", + "test_pred = model.get_anomaly_label(time_series=test_data)" + ] + }, + { + "cell_type": "markdown", + "id": "28de2123", + "metadata": {}, + "source": [ + "Next, we visualize the model's predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "869a78e7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from merlion.plot import plot_anoms\n", + "import matplotlib.pyplot as plt\n", + "fig, ax = model.plot_anomaly(time_series=test_data)\n", + "plot_anoms(ax=ax, anomaly_labels=test_labels)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d5343929", + "metadata": {}, + "source": [ + "Finally, we can quantitatively evaluate the model. The precision and recall come from the fact that the model fired 3 alarms, with 2 true positives, 1 false negative, and 1 false positive. We also evaluate the mean time the model took to detect each anomaly that it correctly detected." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7e98f175", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Precision: 0.6667, Recall: 0.6667, F1: 0.6667\n", + "Mean Time To Detect: 1 days 10:25:00\n" + ] + } + ], + "source": [ + "from merlion.evaluate.anomaly import TSADMetric\n", + "p = TSADMetric.Precision.value(ground_truth=test_labels, predict=test_pred)\n", + "r = TSADMetric.Recall.value(ground_truth=test_labels, predict=test_pred)\n", + "f1 = TSADMetric.F1.value(ground_truth=test_labels, predict=test_pred)\n", + "mttd = TSADMetric.MeanTimeToDetect.value(ground_truth=test_labels, predict=test_pred)\n", + "print(f\"Precision: {p:.4f}, Recall: {r:.4f}, F1: {f1:.4f}\\n\"\n", + " f\"Mean Time To Detect: {mttd}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/v2.0.2/tutorials/anomaly/1_AnomalyFeatures.html b/v2.0.2/tutorials/anomaly/1_AnomalyFeatures.html new file mode 100644 index 000000000..38d800cb9 --- /dev/null +++ b/v2.0.2/tutorials/anomaly/1_AnomalyFeatures.html @@ -0,0 +1,1373 @@ + + + + + + How to Use Anomaly Detectors in Merlion — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

How to Use Anomaly Detectors in Merlion

+

This notebook will guide you through using all the key features of anomaly detectors in Merlion. Specifically, we will explain

+
    +

  1. Initializing an anomaly detection model (including ensembles)

    2. +

  2. Training the model

    3. +

  3. Producing a series of anomaly scores with the model

    4. +

  4. Quantitatively evaluating the model

    5. +

  5. Visualizing the model’s predictions

    6. +

  6. Saving and loading a trained model

    7. +

  7. Simulating the live deployment of a model using a TSADEvaluator

    8. +
+

We will be using a single example time series for this whole notebook. We load and visualize it now:

+
+
[1]:
+
+
+
import matplotlib.pyplot as plt
+import numpy as np
+
+from merlion.plot import plot_anoms
+from merlion.utils import TimeSeries
+from ts_datasets.anomaly import NAB
+
+np.random.seed(1234)
+
+# This is a time series with anomalies in both the train and test split.
+# time_series and metadata are both time-indexed pandas DataFrames.
+time_series, metadata = NAB(subset="realKnownCause")[3]
+
+# Visualize the full time series
+fig = plt.figure(figsize=(10, 6))
+ax = fig.add_subplot(111)
+ax.plot(time_series)
+
+# Label the train/test split with a dashed line & plot anomalies
+ax.axvline(metadata[metadata.trainval].index[-1], ls="--", lw=2, c="k")
+plot_anoms(ax, TimeSeries.from_pd(metadata.anomaly))
+
+
+
+
+
+
+
+
+Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/ec2_request_latency_system_failure.csv (index 2) has timestamp duplicates. Kept first values.
+Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/machine_temperature_system_failure.csv (index 3) has timestamp duplicates. Kept first values.
+
+
+
+
[1]:
+
+
+
+
+<AxesSubplot:>
+
+
+
+
+
+
+../../_images/tutorials_anomaly_1_AnomalyFeatures_1_2.png +
+
+
+
[2]:
+
+
+
from merlion.utils import TimeSeries
+
+# Get training split
+train = time_series[metadata.trainval]
+train_data = TimeSeries.from_pd(train)
+train_labels = TimeSeries.from_pd(metadata[metadata.trainval].anomaly)
+
+# Get testing split
+test = time_series[~metadata.trainval]
+test_data = TimeSeries.from_pd(test)
+test_labels = TimeSeries.from_pd(metadata[~metadata.trainval].anomaly)
+
+
+
+
+

Model Initialization

+

In this notebook, we will use three different anomaly detection models:

+
    +

  1. Isolation Forest (a classic anomaly detection model)

    2. +

  2. WindStats (an in-house model that divides each week into windows of a specified size, and compares time series values to the historical values in the appropriate window)

    3. +

  3. Prophet (Facebook’s popular forecasting model, adapted for anomaly detection.

    4. +
+

Let’s start by initializing each of them:

+
+
[3]:
+
+
+
# Import models & configs
+from merlion.models.anomaly.isolation_forest import IsolationForest, IsolationForestConfig
+from merlion.models.anomaly.windstats import WindStats, WindStatsConfig
+from merlion.models.anomaly.forecast_based.prophet import ProphetDetector, ProphetDetectorConfig
+
+# Import a post-rule for thresholding
+from merlion.post_process.threshold import AggregateAlarms
+
+# Import a data processing transform
+from merlion.transform.moving_average import DifferenceTransform
+
+# All models are initialized using the syntax ModelClass(config), where config
+# is a model-specific configuration object. This is where you specify any
+# algorithm-specific hyperparameters, any data pre-processing transforms, and
+# the post-rule you want to use to post-process the anomaly scores (to reduce
+# noisiness when firing alerts).
+
+# We initialize isolation forest using the default config
+config1 = IsolationForestConfig()
+model1  = IsolationForest(config1)
+
+# We use a WindStats model that splits each week into windows of 60 minutes
+# each. Anomaly scores in Merlion correspond to z-scores. By default, we would
+# like to fire an alert for any 4-sigma event, so we specify a threshold rule
+# which achieves this.
+config2 = WindStatsConfig(wind_sz=60, threshold=AggregateAlarms(alm_threshold=4))
+model2  = WindStats(config2)
+
+# Prophet is a popular forecasting algorithm. Here, we specify that we would like
+# to pre-processes the input time series by applying a difference transform,
+# before running the model on it.
+config3 = ProphetDetectorConfig(transform=DifferenceTransform())
+model3  = ProphetDetector(config3)
+
+
+
+

Now that we have initialized the individual models, we will also combine them in an ensemble. We set this ensemble’s detection threshold to fire alerts for 4-sigma events (the same as WindStats).

+
+
[4]:
+
+
+
from merlion.models.ensemble.anomaly import DetectorEnsemble, DetectorEnsembleConfig
+
+ensemble_config = DetectorEnsembleConfig(threshold=AggregateAlarms(alm_threshold=4))
+ensemble = DetectorEnsemble(config=ensemble_config, models=[model1, model2, model3])
+
+
+
+
+
+

Model Training

+

All anomaly detection models (and ensembles) share the same API for training. The train() method returns the model’s predicted anomaly scores on the training data. Note that you may optionally specify configs that modify the protocol used to train the model’s post-rule! You may optionally specify ground truth anomaly labels as well (if you have them), but they are not needed. We give examples of all these behaviors below.

+
+
[5]:
+
+
+
from merlion.evaluate.anomaly import TSADMetric
+
+# Train IsolationForest in the default way, using the ground truth anomaly labels
+# to set the post-rule's threshold
+print(f"Training {type(model1).__name__}...")
+train_scores_1 = model1.train(train_data=train_data, anomaly_labels=train_labels)
+
+# Train WindStats completely unsupervised (this retains our anomaly detection
+# default anomaly detection threshold of 4)
+print(f"\nTraining {type(model2).__name__}...")
+train_scores_2 = model2.train(train_data=train_data, anomaly_labels=None)
+
+# Train Prophet with the ground truth anomaly labels, with a post-rule
+# trained to optimize Precision score
+print(f"\nTraining {type(model3).__name__}...")
+post_rule_train_config_3 = dict(metric=TSADMetric.F1)
+train_scores_3 = model3.train(
+    train_data=train_data, anomaly_labels=train_labels,
+    post_rule_train_config=post_rule_train_config_3)
+
+# We consider an unsupervised ensemble, which combines the anomaly scores
+# returned by the models & sets a static anomaly detection threshold of 3.
+print("\nTraining ensemble...")
+ensemble_post_rule_train_config = dict(metric=None)
+train_scores_e = ensemble.train(
+    train_data=train_data, anomaly_labels=train_labels,
+    post_rule_train_config=ensemble_post_rule_train_config,
+)
+
+print("Done!")
+
+
+
+
+
+
+
+
+Training IsolationForest...
+
+
+
+
+
+
+
+17:22:24 - cmdstanpy - INFO - Chain [1] start processing
+
+
+
+
+
+
+
+
+Training WindStats...
+
+Training ProphetDetector...
+
+
+
+
+
+
+
+17:22:24 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+
+Training ensemble...
+
+
+
+
+
+
+
+17:22:26 - cmdstanpy - INFO - Chain [1] start processing
+17:22:26 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+Done!
+
+
+
+
+

Model Inference

+

There are two ways to invoke an anomaly detection model: model.get_anomaly_score() returns the model’s raw anomaly scores, while model.get_anomaly_label() returns the model’s post-processed anomaly scores. The post-processing calibrates the anomaly scores to be interpretable as z-scores, and it also sparsifies them such that any nonzero values should be treated as an alert that a particular timestamp is anomalous.

+
+
[6]:
+
+
+
# Here is a full example for the first model, IsolationForest
+scores_1 = model1.get_anomaly_score(test_data)
+scores_1_df = scores_1.to_pd()
+print(f"{type(model1).__name__}.get_anomaly_score() nonzero values (raw)")
+print(scores_1_df[scores_1_df.iloc[:, 0] != 0])
+print()
+
+labels_1 = model1.get_anomaly_label(test_data)
+labels_1_df = labels_1.to_pd()
+print(f"{type(model1).__name__}.get_anomaly_label() nonzero values (post-processed)")
+print(labels_1_df[labels_1_df.iloc[:, 0] != 0])
+print()
+
+print(f"{type(model1).__name__} fires {(labels_1_df.values != 0).sum()} alarms")
+print()
+
+print("Raw scores at the locations where alarms were fired:")
+print(scores_1_df[labels_1_df.iloc[:, 0] != 0])
+print("Post-processed scores are interpretable as z-scores")
+print("Raw scores are challenging to interpret")
+
+
+
+
+
+
+
+
+IsolationForest.get_anomaly_score() nonzero values (raw)
+                     anom_score
+time
+2013-12-14 16:55:00    0.424103
+2013-12-14 17:00:00    0.418938
+2013-12-14 17:05:00    0.484891
+2013-12-14 17:10:00    0.500257
+2013-12-14 17:15:00    0.449213
+...                         ...
+2014-02-19 15:05:00    0.419456
+2014-02-19 15:10:00    0.415807
+2014-02-19 15:15:00    0.406724
+2014-02-19 15:20:00    0.427094
+2014-02-19 15:25:00    0.428348
+
+[19279 rows x 1 columns]
+
+IsolationForest.get_anomaly_label() nonzero values (post-processed)
+                     anom_score
+time
+2013-12-16 16:00:00    3.251397
+2013-12-16 18:35:00    3.681691
+2013-12-27 19:25:00    3.914430
+2013-12-27 23:20:00    3.260543
+2013-12-28 04:15:00    3.738462
+2013-12-28 06:20:00    3.303482
+2014-01-02 10:00:00    3.233514
+2014-01-05 17:50:00    3.791805
+2014-01-12 09:25:00    3.535895
+2014-01-13 10:05:00    3.314500
+2014-01-16 12:50:00    3.850349
+2014-01-24 12:50:00    4.170855
+2014-01-27 17:45:00    3.537919
+2014-01-28 22:00:00    3.451974
+2014-01-30 23:40:00    3.550075
+2014-02-02 23:45:00    3.359105
+2014-02-03 11:55:00    4.175556
+2014-02-05 05:10:00    3.675433
+2014-02-09 11:55:00    4.005116
+2014-02-13 19:15:00    3.247573
+
+IsolationForest fires 20 alarms
+
+Raw scores at the locations where alarms were fired:
+                     anom_score
+time
+2013-12-16 16:00:00    0.701491
+2013-12-16 18:35:00    0.772563
+2013-12-27 19:25:00    0.810997
+2013-12-27 23:20:00    0.702972
+2013-12-28 04:15:00    0.781997
+2013-12-28 06:20:00    0.709952
+2014-01-02 10:00:00    0.698602
+2014-01-05 17:50:00    0.790835
+2014-01-12 09:25:00    0.748293
+2014-01-13 10:05:00    0.711750
+2014-01-16 12:50:00    0.800493
+2014-01-24 12:50:00    0.852493
+2014-01-27 17:45:00    0.748630
+2014-01-28 22:00:00    0.734366
+2014-01-30 23:40:00    0.750652
+2014-02-02 23:45:00    0.719052
+2014-02-03 11:55:00    0.853260
+2014-02-05 05:10:00    0.771522
+2014-02-09 11:55:00    0.825713
+2014-02-13 19:15:00    0.700873
+Post-processed scores are interpretable as z-scores
+Raw scores are challenging to interpret
+
+
+

The same API is shared for all models, including ensembles.

+
+
[7]:
+
+
+
scores_2 = model2.get_anomaly_score(test_data)
+labels_2 = model2.get_anomaly_label(test_data)
+
+
+
+
+
[8]:
+
+
+
scores_3 = model3.get_anomaly_score(test_data)
+labels_3 = model3.get_anomaly_label(test_data)
+
+
+
+
+
[9]:
+
+
+
scores_e = ensemble.get_anomaly_score(test_data)
+labels_e = ensemble.get_anomaly_label(test_data)
+
+
+
+
+
+

Quantitative Evaluation

+

It is fairly transparent to visualize a model’s predicted anomaly scores and also quantitatively evaluate its anomaly labels. For evaluation, we use specialized definitions of precision, recall, and F1 as revised point-adjusted metrics (see the technical report for more details). We also consider the mean time to detect anomalies.

+

In general, you may use the TSADMetric enum to compute evaluation metrics for a time series using the syntax

+
TSADMetric.<metric_name>.value(ground_truth=ground_truth, predict=anomaly_labels)
+
+
+

where <metric_name> is the name of the evaluation metric (see the API docs for details and more options), ground_truth is a time series of ground truth anomaly labels, and anomaly_labels is the output of model.get_anomaly_label().

+
+
[10]:
+
+
+
from merlion.evaluate.anomaly import TSADMetric
+
+for model, labels in [(model1, labels_1), (model2, labels_2), (model3, labels_3), (ensemble, labels_e)]:
+    print(f"{type(model).__name__}")
+    precision = TSADMetric.Precision.value(ground_truth=test_labels, predict=labels)
+    recall = TSADMetric.Recall.value(ground_truth=test_labels, predict=labels)
+    f1 = TSADMetric.F1.value(ground_truth=test_labels, predict=labels)
+    mttd = TSADMetric.MeanTimeToDetect.value(ground_truth=test_labels, predict=labels)
+    print(f"Precision: {precision:.4f}")
+    print(f"Recall:    {recall:.4f}")
+    print(f"F1:        {f1:.4f}")
+    print(f"MTTD:      {mttd}")
+    print()
+
+
+
+
+
+
+
+
+IsolationForest
+Precision: 0.1667
+Recall:    1.0000
+F1:        0.2857
+MTTD:      0 days 23:31:40
+
+WindStats
+Precision: 0.0270
+Recall:    1.0000
+F1:        0.0526
+MTTD:      0 days 12:01:40
+
+ProphetDetector
+Precision: 0.2000
+Recall:    0.6667
+F1:        0.3077
+MTTD:      1 days 10:22:30
+
+DetectorEnsemble
+Precision: 0.4000
+Recall:    0.6667
+F1:        0.5000
+MTTD:      1 days 10:22:30
+
+
+
+

Since the individual models are trained to optimize F1 directly, they all have low precision, high recall, and a mean time to detect of around 1 day. However, by instead training the individual models to optimize precision, and training a model combination unit to optimize F1, we are able to greatly increase the precision and F1 score, at the cost of a lower recall and higher mean time to detect.

+
+
+

Model Visualization

+

Let’s now visualize the model predictions that led to these outcomes. The option filter_scores=True means that we want to plot the post-processed anomaly scores (i.e. returned by model.get_anomaly_label()). You may instead specify filter_scores=False to visualize the raw anomaly scores.

+
+
[11]:
+
+
+
for model in [model1, model2, model3]:
+    print(type(model).__name__)
+    fig, ax = model.plot_anomaly(
+        time_series=test_data, time_series_prev=train_data,
+        filter_scores=True, plot_time_series_prev=True)
+    plot_anoms(ax=ax, anomaly_labels=test_labels)
+    plt.show()
+    print()
+
+
+
+
+
+
+
+
+IsolationForest
+
+
+
+
+
+
+../../_images/tutorials_anomaly_1_AnomalyFeatures_19_1.png +
+
+
+
+
+
+
+
+WindStats
+
+
+
+
+
+
+../../_images/tutorials_anomaly_1_AnomalyFeatures_19_3.png +
+
+
+
+
+
+
+
+ProphetDetector
+
+
+
+
+
+
+../../_images/tutorials_anomaly_1_AnomalyFeatures_19_5.png +
+
+
+
+
+
+
+
+
+
+

So all the individual models generate quite a few false positives. Let’s see how the ensemble does:

+
+
[12]:
+
+
+
fig, ax = ensemble.plot_anomaly(
+    time_series=test_data, time_series_prev=train_data,
+    filter_scores=True, plot_time_series_prev=True)
+plot_anoms(ax=ax, anomaly_labels=test_labels)
+plt.show()
+
+
+
+
+
+
+
+../../_images/tutorials_anomaly_1_AnomalyFeatures_21_0.png +
+
+

So the ensemble misses one of the three anomalies in the test split, but it also greatly reduces the number of false positives relative to the other models.

+
+
+

Saving & Loading Models

+

All models have a save() method and load() class method. Models may also be loaded with the assistance of the ModelFactory, which works for arbitrary models. The save() method creates a new directory at the specified path, where it saves a json file representing the model’s config, as well as a binary file for the model’s state.

+

We will demonstrate these behaviors using our IsolationForest model (model1) for concreteness. Note that the config explicitly tracks the transform (to pre-process the data), the calibrator (to transform raw anomaly scores into z-scores), the thresholding rule (to sparsify the calibrated anomaly scores).

+
+
[13]:
+
+
+
import json
+import os
+import pprint
+from merlion.models.factory import ModelFactory
+
+# Save the model
+os.makedirs("models", exist_ok=True)
+path = os.path.join("models", "isf")
+model1.save(path)
+
+# Print the config saved
+pp = pprint.PrettyPrinter()
+with open(os.path.join(path, "config.json")) as f:
+    print(f"{type(model1).__name__} Config")
+    pp.pprint(json.load(f))
+
+# Load the model using Prophet.load()
+model2_loaded = IsolationForest.load(dirname=path)
+
+# Load the model using the ModelFactory
+model2_factory_loaded = ModelFactory.load(name="IsolationForest", model_path=path)
+
+
+
+
+
+
+
+
+IsolationForest Config
+{'calibrator': {'abs_score': True,
+                'anchors': [[0.38992633996347176, 0.0],
+                            [0.4187750781361715, 0.5],
+                            [0.445336977389891, 1.0],
+                            [0.47974261897360404, 1.5],
+                            [0.5271631189090943, 2.0],
+                            [0.8301789920204418, 4.032894437734716],
+                            [1.0, 5.032894437734716]],
+                'max_score': 1.0,
+                'name': 'AnomScoreCalibrator'},
+ 'dim': 1,
+ 'enable_calibrator': True,
+ 'enable_threshold': True,
+ 'max_n_samples': 1.0,
+ 'n_estimators': 100,
+ 'threshold': {'abs_score': True,
+               'alm_suppress_minutes': 120,
+               'alm_threshold': 3.2263155501877727,
+               'alm_window_minutes': 60,
+               'min_alm_in_window': 2,
+               'name': 'AggregateAlarms'},
+ 'transform': {'name': 'TransformSequence',
+               'transforms': [{'name': 'DifferenceTransform'},
+                              {'multivar_skip': True,
+                               'name': 'Shingle',
+                               'size': 2,
+                               'stride': 1}]}}
+
+
+

We can do the same exact thing with ensembles! Note that the ensemble stores its underlying models in a nested structure. This is all reflected in the config.

+
+
[14]:
+
+
+
# Save the ensemble
+path = os.path.join("models", "ensemble")
+ensemble.save(path)
+
+# Print the config saved. Note that we've saved all individual models,
+# and their paths are specified under the model_paths key.
+pp = pprint.PrettyPrinter()
+with open(os.path.join(path, "config.json")) as f:
+    print(f"Ensemble Config")
+    pp.pprint(json.load(f))
+
+# Load the selector
+selector_loaded = DetectorEnsemble.load(dirname=path)
+
+# Load the selector using the ModelFactory
+selector_factory_loaded = ModelFactory.load(name="DetectorEnsemble", model_path=path)
+
+
+
+
+
+
+
+
+Ensemble Config
+{'calibrator': {'abs_score': True,
+                'anchors': None,
+                'max_score': 1000,
+                'name': 'AnomScoreCalibrator'},
+ 'combiner': {'_override_models_used': {},
+              'abs_score': True,
+              'n_models': 3,
+              'name': 'Mean'},
+ 'dim': 1,
+ 'enable_calibrator': False,
+ 'enable_threshold': True,
+ 'models': [{'calibrator': {'abs_score': True,
+                            'anchors': [[0.38992633996347176, 0.0],
+                                        [0.4187750781361715, 0.5],
+                                        [0.445336977389891, 1.0],
+                                        [0.47974261897360404, 1.5],
+                                        [0.5271631189090943, 2.0],
+                                        [0.8301789920204418, 4.032894437734716],
+                                        [1.0, 5.032894437734716]],
+                            'max_score': 1.0,
+                            'name': 'AnomScoreCalibrator'},
+             'dim': 1,
+             'enable_calibrator': True,
+             'enable_threshold': False,
+             'max_n_samples': 1.0,
+             'n_estimators': 100,
+             'name': 'IsolationForest',
+             'threshold': {'abs_score': True,
+                           'alm_suppress_minutes': 120,
+                           'alm_threshold': 3.0,
+                           'alm_window_minutes': 60,
+                           'min_alm_in_window': 2,
+                           'name': 'AggregateAlarms'},
+             'transform': {'name': 'TransformSequence',
+                           'transforms': [{'name': 'DifferenceTransform'},
+                                          {'multivar_skip': True,
+                                           'name': 'Shingle',
+                                           'size': 2,
+                                           'stride': 1}]}},
+            {'calibrator': {'abs_score': True,
+                            'anchors': [[0.0004858784421674658, 0.0],
+                                        [0.4318659926885851, 0.5],
+                                        [0.9774407588312237, 1.0],
+                                        [1.4231054875246496, 1.5],
+                                        [1.7393725195754337, 2.0],
+                                        [2.4271291767175622, 4.032894437734716],
+                                        [4.8542583534351245,
+                                         5.032894437734716]],
+                            'max_score': 1000,
+                            'name': 'AnomScoreCalibrator'},
+             'dim': 1,
+             'enable_calibrator': True,
+             'enable_threshold': False,
+             'max_day': 4,
+             'name': 'WindStats',
+             'threshold': {'abs_score': True,
+                           'alm_suppress_minutes': 120,
+                           'alm_threshold': 4,
+                           'alm_window_minutes': 60,
+                           'min_alm_in_window': 2,
+                           'name': 'AggregateAlarms'},
+             'transform': {'name': 'DifferenceTransform'},
+             'wind_sz': 60},
+            {'calibrator': {'abs_score': True,
+                            'anchors': [[0.00040425650796231867, 0.0],
+                                        [0.5103916318368437, 0.5],
+                                        [1.0369977090370754, 1.0],
+                                        [1.5325298959635636, 1.5],
+                                        [1.9215534761800885, 2.0],
+                                        [5.2965340676146635, 4.032894437734716],
+                                        [10.593068135229327,
+                                         5.032894437734716]],
+                            'max_score': 1000,
+                            'name': 'AnomScoreCalibrator'},
+             'daily_seasonality': 'auto',
+             'dim': 1,
+             'enable_calibrator': True,
+             'enable_threshold': False,
+             'exog_aggregation_policy': 'Mean',
+             'exog_missing_value_policy': 'ZFill',
+             'exog_transform': {'bias': None,
+                                'name': 'MeanVarNormalize',
+                                'normalize_bias': True,
+                                'normalize_scale': True,
+                                'scale': None},
+             'holidays': None,
+             'invert_transform': False,
+             'max_forecast_steps': None,
+             'name': 'ProphetDetector',
+             'seasonality_mode': 'additive',
+             'target_seq_index': 0,
+             'threshold': {'abs_score': True,
+                           'alm_suppress_minutes': 120,
+                           'alm_threshold': 3,
+                           'alm_window_minutes': 60,
+                           'min_alm_in_window': 2,
+                           'name': 'AggregateAlarms'},
+             'transform': {'name': 'DifferenceTransform'},
+             'uncertainty_samples': 100,
+             'weekly_seasonality': 'auto',
+             'yearly_seasonality': 'auto'}],
+ 'threshold': {'abs_score': True,
+               'alm_suppress_minutes': 120,
+               'alm_threshold': 4,
+               'alm_window_minutes': 60,
+               'min_alm_in_window': 2,
+               'name': 'AggregateAlarms'},
+ 'transform': {'name': 'Identity'}}
+
+
+
+
+

Simulating Live Model Deployment

+

A typical model deployment scenario is as follows: 1. Train an initial model on some recent historical data, optionally with labels. 1. At a regular interval retrain_freq (e.g. once per week), retrain the entire model unsupervised (i.e. with no labels) on the most recent data. 1. Obtain the model’s predicted anomaly scores for the time series values that occur between re-trainings. We perform this operation in batch, but a deployment scenario may do this in streaming. 1. Optionally, specify +a maximum amount of data (train_window) that the model should use for training (e.g. the most recent 2 weeks of data).

+

We provide a TSADEvaluator object which simulates the above deployment scenario, and also allows a user to evaluate the quality of the forecaster according to an evaluation metric of their choice. We illustrate an example below using the ensemble.

+
+
[15]:
+
+
+
# Initialize the evaluator
+from merlion.evaluate.anomaly import TSADEvaluator, TSADEvaluatorConfig
+
+evaluator = TSADEvaluator(model=ensemble, config=TSADEvaluatorConfig(retrain_freq="7d"))
+
+
+
+
+
[16]:
+
+
+
# The kwargs we would provide to ensemble.train() for the initial training
+# Note that we are training the ensemble unsupervised.
+train_kwargs = {"anomaly_labels": None}
+
+# We will use the default kwargs for re-training (these leave the
+# post-rules unchanged, since there are no new labels)
+retrain_kwargs = None
+
+# We call evaluator.get_predict() to get the time series of anomaly scores
+# produced by the anomaly detector when deployed in this manner
+train_scores, test_scores = evaluator.get_predict(
+    train_vals=train_data, test_vals=test_data,
+    train_kwargs=train_kwargs, retrain_kwargs=retrain_kwargs
+)
+
+
+
+
+
+
+
+
+17:22:42 - cmdstanpy - INFO - Chain [1] start processing
+17:22:43 - cmdstanpy - INFO - Chain [1] done processing
+TSADEvaluator:  10%|█         | 604800/5783700 [00:00<00:03, 1307785.29it/s]17:22:44 - cmdstanpy - INFO - Chain [1] start processing
+17:22:44 - cmdstanpy - INFO - Chain [1] done processing
+TSADEvaluator:  21%|██        | 1209600/5783700 [00:02<00:08, 551941.29it/s]17:22:46 - cmdstanpy - INFO - Chain [1] start processing
+17:22:46 - cmdstanpy - INFO - Chain [1] done processing
+TSADEvaluator:  31%|███▏      | 1814400/5783700 [00:04<00:10, 365146.12it/s]17:22:48 - cmdstanpy - INFO - Chain [1] start processing
+17:22:48 - cmdstanpy - INFO - Chain [1] done processing
+TSADEvaluator:  42%|████▏     | 2419200/5783700 [00:06<00:09, 339456.87it/s]17:22:50 - cmdstanpy - INFO - Chain [1] start processing
+17:22:51 - cmdstanpy - INFO - Chain [1] done processing
+TSADEvaluator:  52%|█████▏    | 3024000/5783700 [00:08<00:09, 291111.79it/s]17:22:53 - cmdstanpy - INFO - Chain [1] start processing
+17:22:53 - cmdstanpy - INFO - Chain [1] done processing
+TSADEvaluator:  63%|██████▎   | 3628800/5783700 [00:11<00:07, 269495.41it/s]17:22:55 - cmdstanpy - INFO - Chain [1] start processing
+17:22:58 - cmdstanpy - INFO - Chain [1] done processing
+TSADEvaluator:  73%|███████▎  | 4233600/5783700 [00:15<00:07, 206629.47it/s]17:23:00 - cmdstanpy - INFO - Chain [1] start processing
+17:23:01 - cmdstanpy - INFO - Chain [1] done processing
+TSADEvaluator:  84%|████████▎ | 4838400/5783700 [00:19<00:05, 188649.53it/s]17:23:04 - cmdstanpy - INFO - Chain [1] start processing
+17:23:06 - cmdstanpy - INFO - Chain [1] done processing
+TSADEvaluator:  94%|█████████▍| 5443200/5783700 [00:24<00:02, 166964.38it/s]17:23:09 - cmdstanpy - INFO - Chain [1] start processing
+17:23:12 - cmdstanpy - INFO - Chain [1] done processing
+TSADEvaluator: 100%|██████████| 5783700/5783700 [00:29<00:00, 193604.33it/s]
+
+
+
+
[17]:
+
+
+
# Now let's evaluate how we did.
+precision = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.Precision)
+recall    = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.Recall)
+f1        = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.F1)
+mttd      = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.MeanTimeToDetect)
+print("Ensemble Performance")
+print(f"Precision: {precision:.4f}")
+print(f"Recall:    {recall:.4f}")
+print(f"F1:        {f1:.4f}")
+print(f"MTTD:      {mttd}")
+print()
+
+
+
+
+
+
+
+
+Ensemble Performance
+Precision: 0.5000
+Recall:    0.6667
+F1:        0.5714
+MTTD:      1 days 10:25:00
+
+
+
+

In this case, we see that by simply re-training the ensemble weekly in an unsupervised manner, we have increased the precision from \(\frac{2}{5}\) to \(\frac{2}{4}\), while leaving unchanged the recall and mean time to detect. This is due to data drift over time.

+
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/anomaly/1_AnomalyFeatures.ipynb b/v2.0.2/tutorials/anomaly/1_AnomalyFeatures.ipynb new file mode 100644 index 000000000..b0edf0801 --- /dev/null +++ b/v2.0.2/tutorials/anomaly/1_AnomalyFeatures.ipynb @@ -0,0 +1,985 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to Use Anomaly Detectors in Merlion\n", + "\n", + "This notebook will guide you through using all the key features of anomaly detectors in Merlion. Specifically, we will explain\n", + "\n", + "1. Initializing an anomaly detection model (including ensembles)\n", + "1. Training the model\n", + "1. Producing a series of anomaly scores with the model\n", + "1. Quantitatively evaluating the model\n", + "1. Visualizing the model's predictions\n", + "1. Saving and loading a trained model\n", + "1. Simulating the live deployment of a model using a `TSADEvaluator`\n", + "\n", + "We will be using a single example time series for this whole notebook. We load and visualize it now:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/ec2_request_latency_system_failure.csv (index 2) has timestamp duplicates. Kept first values.\n", + "Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/machine_temperature_system_failure.csv (index 3) has timestamp duplicates. Kept first values.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from merlion.plot import plot_anoms\n", + "from merlion.utils import TimeSeries\n", + "from ts_datasets.anomaly import NAB\n", + "\n", + "np.random.seed(1234)\n", + "\n", + "# This is a time series with anomalies in both the train and test split.\n", + "# time_series and metadata are both time-indexed pandas DataFrames.\n", + "time_series, metadata = NAB(subset=\"realKnownCause\")[3]\n", + "\n", + "# Visualize the full time series\n", + "fig = plt.figure(figsize=(10, 6))\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(time_series)\n", + "\n", + "# Label the train/test split with a dashed line & plot anomalies\n", + "ax.axvline(metadata[metadata.trainval].index[-1], ls=\"--\", lw=2, c=\"k\")\n", + "plot_anoms(ax, TimeSeries.from_pd(metadata.anomaly))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from merlion.utils import TimeSeries\n", + "\n", + "# Get training split\n", + "train = time_series[metadata.trainval]\n", + "train_data = TimeSeries.from_pd(train)\n", + "train_labels = TimeSeries.from_pd(metadata[metadata.trainval].anomaly)\n", + "\n", + "# Get testing split\n", + "test = time_series[~metadata.trainval]\n", + "test_data = TimeSeries.from_pd(test)\n", + "test_labels = TimeSeries.from_pd(metadata[~metadata.trainval].anomaly)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Initialization\n", + "\n", + "In this notebook, we will use three different anomaly detection models:\n", + "\n", + "1. Isolation Forest (a classic anomaly detection model)\n", + "2. WindStats (an in-house model that divides each week into windows of a specified size, and compares time series values to the historical values in the appropriate window)\n", + "3. Prophet (Facebook's popular forecasting model, adapted for anomaly detection.\n", + "\n", + "Let's start by initializing each of them:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import models & configs\n", + "from merlion.models.anomaly.isolation_forest import IsolationForest, IsolationForestConfig\n", + "from merlion.models.anomaly.windstats import WindStats, WindStatsConfig\n", + "from merlion.models.anomaly.forecast_based.prophet import ProphetDetector, ProphetDetectorConfig\n", + "\n", + "# Import a post-rule for thresholding\n", + "from merlion.post_process.threshold import AggregateAlarms\n", + "\n", + "# Import a data processing transform\n", + "from merlion.transform.moving_average import DifferenceTransform\n", + "\n", + "# All models are initialized using the syntax ModelClass(config), where config\n", + "# is a model-specific configuration object. This is where you specify any\n", + "# algorithm-specific hyperparameters, any data pre-processing transforms, and\n", + "# the post-rule you want to use to post-process the anomaly scores (to reduce\n", + "# noisiness when firing alerts). \n", + "\n", + "# We initialize isolation forest using the default config\n", + "config1 = IsolationForestConfig()\n", + "model1 = IsolationForest(config1)\n", + "\n", + "# We use a WindStats model that splits each week into windows of 60 minutes\n", + "# each. Anomaly scores in Merlion correspond to z-scores. By default, we would\n", + "# like to fire an alert for any 4-sigma event, so we specify a threshold rule\n", + "# which achieves this.\n", + "config2 = WindStatsConfig(wind_sz=60, threshold=AggregateAlarms(alm_threshold=4))\n", + "model2 = WindStats(config2)\n", + "\n", + "# Prophet is a popular forecasting algorithm. Here, we specify that we would like\n", + "# to pre-processes the input time series by applying a difference transform,\n", + "# before running the model on it.\n", + "config3 = ProphetDetectorConfig(transform=DifferenceTransform())\n", + "model3 = ProphetDetector(config3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have initialized the individual models, we will also combine them in an ensemble. We set this ensemble's detection threshold to fire alerts for 4-sigma events (the same as WindStats)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from merlion.models.ensemble.anomaly import DetectorEnsemble, DetectorEnsembleConfig\n", + "\n", + "ensemble_config = DetectorEnsembleConfig(threshold=AggregateAlarms(alm_threshold=4))\n", + "ensemble = DetectorEnsemble(config=ensemble_config, models=[model1, model2, model3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training\n", + "\n", + "All anomaly detection models (and ensembles) share the same API for training. The `train()` method returns the model's predicted anomaly scores on the training data. Note that you may optionally specify configs that modify the protocol used to train the model's post-rule! You may optionally specify ground truth anomaly labels as well (if you have them), but they are not needed. We give examples of all these behaviors below." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training IsolationForest...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:22:24 - cmdstanpy - INFO - Chain [1] start processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training WindStats...\n", + "\n", + "Training ProphetDetector...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:22:24 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training ensemble...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:22:26 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:22:26 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done!\n" + ] + } + ], + "source": [ + "from merlion.evaluate.anomaly import TSADMetric\n", + "\n", + "# Train IsolationForest in the default way, using the ground truth anomaly labels\n", + "# to set the post-rule's threshold\n", + "print(f\"Training {type(model1).__name__}...\")\n", + "train_scores_1 = model1.train(train_data=train_data, anomaly_labels=train_labels)\n", + "\n", + "# Train WindStats completely unsupervised (this retains our anomaly detection \n", + "# default anomaly detection threshold of 4)\n", + "print(f\"\\nTraining {type(model2).__name__}...\")\n", + "train_scores_2 = model2.train(train_data=train_data, anomaly_labels=None)\n", + "\n", + "# Train Prophet with the ground truth anomaly labels, with a post-rule\n", + "# trained to optimize Precision score\n", + "print(f\"\\nTraining {type(model3).__name__}...\")\n", + "post_rule_train_config_3 = dict(metric=TSADMetric.F1)\n", + "train_scores_3 = model3.train(\n", + " train_data=train_data, anomaly_labels=train_labels,\n", + " post_rule_train_config=post_rule_train_config_3)\n", + "\n", + "# We consider an unsupervised ensemble, which combines the anomaly scores\n", + "# returned by the models & sets a static anomaly detection threshold of 3.\n", + "print(\"\\nTraining ensemble...\")\n", + "ensemble_post_rule_train_config = dict(metric=None)\n", + "train_scores_e = ensemble.train(\n", + " train_data=train_data, anomaly_labels=train_labels,\n", + " post_rule_train_config=ensemble_post_rule_train_config,\n", + ")\n", + "\n", + "print(\"Done!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Inference\n", + "\n", + "There are two ways to invoke an anomaly detection model: `model.get_anomaly_score()` returns the model's raw anomaly scores, while `model.get_anomaly_label()` returns the model's post-processed anomaly scores. The post-processing calibrates the anomaly scores to be interpretable as z-scores, and it also sparsifies them such that any nonzero values should be treated as an alert that a particular timestamp is anomalous." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IsolationForest.get_anomaly_score() nonzero values (raw)\n", + " anom_score\n", + "time \n", + "2013-12-14 16:55:00 0.424103\n", + "2013-12-14 17:00:00 0.418938\n", + "2013-12-14 17:05:00 0.484891\n", + "2013-12-14 17:10:00 0.500257\n", + "2013-12-14 17:15:00 0.449213\n", + "... ...\n", + "2014-02-19 15:05:00 0.419456\n", + "2014-02-19 15:10:00 0.415807\n", + "2014-02-19 15:15:00 0.406724\n", + "2014-02-19 15:20:00 0.427094\n", + "2014-02-19 15:25:00 0.428348\n", + "\n", + "[19279 rows x 1 columns]\n", + "\n", + "IsolationForest.get_anomaly_label() nonzero values (post-processed)\n", + " anom_score\n", + "time \n", + "2013-12-16 16:00:00 3.251397\n", + "2013-12-16 18:35:00 3.681691\n", + "2013-12-27 19:25:00 3.914430\n", + "2013-12-27 23:20:00 3.260543\n", + "2013-12-28 04:15:00 3.738462\n", + "2013-12-28 06:20:00 3.303482\n", + "2014-01-02 10:00:00 3.233514\n", + "2014-01-05 17:50:00 3.791805\n", + "2014-01-12 09:25:00 3.535895\n", + "2014-01-13 10:05:00 3.314500\n", + "2014-01-16 12:50:00 3.850349\n", + "2014-01-24 12:50:00 4.170855\n", + "2014-01-27 17:45:00 3.537919\n", + "2014-01-28 22:00:00 3.451974\n", + "2014-01-30 23:40:00 3.550075\n", + "2014-02-02 23:45:00 3.359105\n", + "2014-02-03 11:55:00 4.175556\n", + "2014-02-05 05:10:00 3.675433\n", + "2014-02-09 11:55:00 4.005116\n", + "2014-02-13 19:15:00 3.247573\n", + "\n", + "IsolationForest fires 20 alarms\n", + "\n", + "Raw scores at the locations where alarms were fired:\n", + " anom_score\n", + "time \n", + "2013-12-16 16:00:00 0.701491\n", + "2013-12-16 18:35:00 0.772563\n", + "2013-12-27 19:25:00 0.810997\n", + "2013-12-27 23:20:00 0.702972\n", + "2013-12-28 04:15:00 0.781997\n", + "2013-12-28 06:20:00 0.709952\n", + "2014-01-02 10:00:00 0.698602\n", + "2014-01-05 17:50:00 0.790835\n", + "2014-01-12 09:25:00 0.748293\n", + "2014-01-13 10:05:00 0.711750\n", + "2014-01-16 12:50:00 0.800493\n", + "2014-01-24 12:50:00 0.852493\n", + "2014-01-27 17:45:00 0.748630\n", + "2014-01-28 22:00:00 0.734366\n", + "2014-01-30 23:40:00 0.750652\n", + "2014-02-02 23:45:00 0.719052\n", + "2014-02-03 11:55:00 0.853260\n", + "2014-02-05 05:10:00 0.771522\n", + "2014-02-09 11:55:00 0.825713\n", + "2014-02-13 19:15:00 0.700873\n", + "Post-processed scores are interpretable as z-scores\n", + "Raw scores are challenging to interpret\n" + ] + } + ], + "source": [ + "# Here is a full example for the first model, IsolationForest\n", + "scores_1 = model1.get_anomaly_score(test_data)\n", + "scores_1_df = scores_1.to_pd()\n", + "print(f\"{type(model1).__name__}.get_anomaly_score() nonzero values (raw)\")\n", + "print(scores_1_df[scores_1_df.iloc[:, 0] != 0])\n", + "print()\n", + "\n", + "labels_1 = model1.get_anomaly_label(test_data)\n", + "labels_1_df = labels_1.to_pd()\n", + "print(f\"{type(model1).__name__}.get_anomaly_label() nonzero values (post-processed)\")\n", + "print(labels_1_df[labels_1_df.iloc[:, 0] != 0])\n", + "print()\n", + "\n", + "print(f\"{type(model1).__name__} fires {(labels_1_df.values != 0).sum()} alarms\")\n", + "print()\n", + "\n", + "print(\"Raw scores at the locations where alarms were fired:\")\n", + "print(scores_1_df[labels_1_df.iloc[:, 0] != 0])\n", + "print(\"Post-processed scores are interpretable as z-scores\")\n", + "print(\"Raw scores are challenging to interpret\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The same API is shared for all models, including ensembles." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "scores_2 = model2.get_anomaly_score(test_data)\n", + "labels_2 = model2.get_anomaly_label(test_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "scores_3 = model3.get_anomaly_score(test_data)\n", + "labels_3 = model3.get_anomaly_label(test_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "scores_e = ensemble.get_anomaly_score(test_data)\n", + "labels_e = ensemble.get_anomaly_label(test_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quantitative Evaluation\n", + "\n", + "It is fairly transparent to visualize a model's predicted anomaly scores and also quantitatively evaluate its anomaly labels. For evaluation, we use specialized definitions of precision, recall, and F1 as revised point-adjusted metrics (see the technical report for more details). We also consider the mean time to detect anomalies.\n", + "\n", + "In general, you may use the `TSADMetric` enum to compute evaluation metrics for a time series using the syntax\n", + "```\n", + "TSADMetric..value(ground_truth=ground_truth, predict=anomaly_labels)\n", + "```\n", + "where `` is the name of the evaluation metric (see the API docs for details and more options), `ground_truth` is a time series of ground truth anomaly labels, and `anomaly_labels` is the output of `model.get_anomaly_label()`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IsolationForest\n", + "Precision: 0.1667\n", + "Recall: 1.0000\n", + "F1: 0.2857\n", + "MTTD: 0 days 23:31:40\n", + "\n", + "WindStats\n", + "Precision: 0.0270\n", + "Recall: 1.0000\n", + "F1: 0.0526\n", + "MTTD: 0 days 12:01:40\n", + "\n", + "ProphetDetector\n", + "Precision: 0.2000\n", + "Recall: 0.6667\n", + "F1: 0.3077\n", + "MTTD: 1 days 10:22:30\n", + "\n", + "DetectorEnsemble\n", + "Precision: 0.4000\n", + "Recall: 0.6667\n", + "F1: 0.5000\n", + "MTTD: 1 days 10:22:30\n", + "\n" + ] + } + ], + "source": [ + "from merlion.evaluate.anomaly import TSADMetric\n", + "\n", + "for model, labels in [(model1, labels_1), (model2, labels_2), (model3, labels_3), (ensemble, labels_e)]:\n", + " print(f\"{type(model).__name__}\")\n", + " precision = TSADMetric.Precision.value(ground_truth=test_labels, predict=labels)\n", + " recall = TSADMetric.Recall.value(ground_truth=test_labels, predict=labels)\n", + " f1 = TSADMetric.F1.value(ground_truth=test_labels, predict=labels)\n", + " mttd = TSADMetric.MeanTimeToDetect.value(ground_truth=test_labels, predict=labels)\n", + " print(f\"Precision: {precision:.4f}\")\n", + " print(f\"Recall: {recall:.4f}\")\n", + " print(f\"F1: {f1:.4f}\")\n", + " print(f\"MTTD: {mttd}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the individual models are trained to optimize F1 directly, they all have low precision, high recall, and a mean time to detect of around 1 day. However, by instead training the individual models to optimize precision, and training a model combination unit to optimize F1, we are able to greatly increase the precision and F1 score, at the cost of a lower recall and higher mean time to detect." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Visualization\n", + "\n", + "Let's now visualize the model predictions that led to these outcomes. The option `filter_scores=True` means that we want to plot the post-processed anomaly scores (i.e. returned by `model.get_anomaly_label()`). You may instead specify `filter_scores=False` to visualize the raw anomaly scores." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IsolationForest\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "ProphetDetector\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "for model in [model1, model2, model3]:\n", + " print(type(model).__name__)\n", + " fig, ax = model.plot_anomaly(\n", + " time_series=test_data, time_series_prev=train_data,\n", + " filter_scores=True, plot_time_series_prev=True)\n", + " plot_anoms(ax=ax, anomaly_labels=test_labels)\n", + " plt.show()\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So all the individual models generate quite a few false positives. Let's see how the ensemble does:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = ensemble.plot_anomaly(\n", + " time_series=test_data, time_series_prev=train_data,\n", + " filter_scores=True, plot_time_series_prev=True)\n", + "plot_anoms(ax=ax, anomaly_labels=test_labels)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So the ensemble misses one of the three anomalies in the test split, but it also greatly reduces the number of false positives relative to the other models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving & Loading Models\n", + "\n", + "All models have a `save()` method and `load()` class method. Models may also be loaded with the assistance of the `ModelFactory`, which works for arbitrary models. The `save()` method creates a new directory at the specified path, where it saves a `json` file representing the model's config, as well as a binary file for the model's state.\n", + "\n", + "We will demonstrate these behaviors using our `IsolationForest` model (`model1`) for concreteness. Note that the config explicitly tracks the transform (to pre-process the data), the calibrator (to transform raw anomaly scores into z-scores), the thresholding rule (to sparsify the calibrated anomaly scores)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IsolationForest Config\n", + "{'calibrator': {'abs_score': True,\n", + " 'anchors': [[0.38992633996347176, 0.0],\n", + " [0.4187750781361715, 0.5],\n", + " [0.445336977389891, 1.0],\n", + " [0.47974261897360404, 1.5],\n", + " [0.5271631189090943, 2.0],\n", + " [0.8301789920204418, 4.032894437734716],\n", + " [1.0, 5.032894437734716]],\n", + " 'max_score': 1.0,\n", + " 'name': 'AnomScoreCalibrator'},\n", + " 'dim': 1,\n", + " 'enable_calibrator': True,\n", + " 'enable_threshold': True,\n", + " 'max_n_samples': 1.0,\n", + " 'n_estimators': 100,\n", + " 'threshold': {'abs_score': True,\n", + " 'alm_suppress_minutes': 120,\n", + " 'alm_threshold': 3.2263155501877727,\n", + " 'alm_window_minutes': 60,\n", + " 'min_alm_in_window': 2,\n", + " 'name': 'AggregateAlarms'},\n", + " 'transform': {'name': 'TransformSequence',\n", + " 'transforms': [{'name': 'DifferenceTransform'},\n", + " {'multivar_skip': True,\n", + " 'name': 'Shingle',\n", + " 'size': 2,\n", + " 'stride': 1}]}}\n" + ] + } + ], + "source": [ + "import json\n", + "import os\n", + "import pprint\n", + "from merlion.models.factory import ModelFactory\n", + "\n", + "# Save the model\n", + "os.makedirs(\"models\", exist_ok=True)\n", + "path = os.path.join(\"models\", \"isf\")\n", + "model1.save(path)\n", + "\n", + "# Print the config saved\n", + "pp = pprint.PrettyPrinter()\n", + "with open(os.path.join(path, \"config.json\")) as f:\n", + " print(f\"{type(model1).__name__} Config\")\n", + " pp.pprint(json.load(f))\n", + "\n", + "# Load the model using Prophet.load()\n", + "model2_loaded = IsolationForest.load(dirname=path)\n", + "\n", + "# Load the model using the ModelFactory\n", + "model2_factory_loaded = ModelFactory.load(name=\"IsolationForest\", model_path=path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can do the same exact thing with ensembles! Note that the ensemble stores its underlying models in a nested structure. This is all reflected in the config." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ensemble Config\n", + "{'calibrator': {'abs_score': True,\n", + " 'anchors': None,\n", + " 'max_score': 1000,\n", + " 'name': 'AnomScoreCalibrator'},\n", + " 'combiner': {'_override_models_used': {},\n", + " 'abs_score': True,\n", + " 'n_models': 3,\n", + " 'name': 'Mean'},\n", + " 'dim': 1,\n", + " 'enable_calibrator': False,\n", + " 'enable_threshold': True,\n", + " 'models': [{'calibrator': {'abs_score': True,\n", + " 'anchors': [[0.38992633996347176, 0.0],\n", + " [0.4187750781361715, 0.5],\n", + " [0.445336977389891, 1.0],\n", + " [0.47974261897360404, 1.5],\n", + " [0.5271631189090943, 2.0],\n", + " [0.8301789920204418, 4.032894437734716],\n", + " [1.0, 5.032894437734716]],\n", + " 'max_score': 1.0,\n", + " 'name': 'AnomScoreCalibrator'},\n", + " 'dim': 1,\n", + " 'enable_calibrator': True,\n", + " 'enable_threshold': False,\n", + " 'max_n_samples': 1.0,\n", + " 'n_estimators': 100,\n", + " 'name': 'IsolationForest',\n", + " 'threshold': {'abs_score': True,\n", + " 'alm_suppress_minutes': 120,\n", + " 'alm_threshold': 3.0,\n", + " 'alm_window_minutes': 60,\n", + " 'min_alm_in_window': 2,\n", + " 'name': 'AggregateAlarms'},\n", + " 'transform': {'name': 'TransformSequence',\n", + " 'transforms': [{'name': 'DifferenceTransform'},\n", + " {'multivar_skip': True,\n", + " 'name': 'Shingle',\n", + " 'size': 2,\n", + " 'stride': 1}]}},\n", + " {'calibrator': {'abs_score': True,\n", + " 'anchors': [[0.0004858784421674658, 0.0],\n", + " [0.4318659926885851, 0.5],\n", + " [0.9774407588312237, 1.0],\n", + " [1.4231054875246496, 1.5],\n", + " [1.7393725195754337, 2.0],\n", + " [2.4271291767175622, 4.032894437734716],\n", + " [4.8542583534351245,\n", + " 5.032894437734716]],\n", + " 'max_score': 1000,\n", + " 'name': 'AnomScoreCalibrator'},\n", + " 'dim': 1,\n", + " 'enable_calibrator': True,\n", + " 'enable_threshold': False,\n", + " 'max_day': 4,\n", + " 'name': 'WindStats',\n", + " 'threshold': {'abs_score': True,\n", + " 'alm_suppress_minutes': 120,\n", + " 'alm_threshold': 4,\n", + " 'alm_window_minutes': 60,\n", + " 'min_alm_in_window': 2,\n", + " 'name': 'AggregateAlarms'},\n", + " 'transform': {'name': 'DifferenceTransform'},\n", + " 'wind_sz': 60},\n", + " {'calibrator': {'abs_score': True,\n", + " 'anchors': [[0.00040425650796231867, 0.0],\n", + " [0.5103916318368437, 0.5],\n", + " [1.0369977090370754, 1.0],\n", + " [1.5325298959635636, 1.5],\n", + " [1.9215534761800885, 2.0],\n", + " [5.2965340676146635, 4.032894437734716],\n", + " [10.593068135229327,\n", + " 5.032894437734716]],\n", + " 'max_score': 1000,\n", + " 'name': 'AnomScoreCalibrator'},\n", + " 'daily_seasonality': 'auto',\n", + " 'dim': 1,\n", + " 'enable_calibrator': True,\n", + " 'enable_threshold': False,\n", + " 'exog_aggregation_policy': 'Mean',\n", + " 'exog_missing_value_policy': 'ZFill',\n", + " 'exog_transform': {'bias': None,\n", + " 'name': 'MeanVarNormalize',\n", + " 'normalize_bias': True,\n", + " 'normalize_scale': True,\n", + " 'scale': None},\n", + " 'holidays': None,\n", + " 'invert_transform': False,\n", + " 'max_forecast_steps': None,\n", + " 'name': 'ProphetDetector',\n", + " 'seasonality_mode': 'additive',\n", + " 'target_seq_index': 0,\n", + " 'threshold': {'abs_score': True,\n", + " 'alm_suppress_minutes': 120,\n", + " 'alm_threshold': 3,\n", + " 'alm_window_minutes': 60,\n", + " 'min_alm_in_window': 2,\n", + " 'name': 'AggregateAlarms'},\n", + " 'transform': {'name': 'DifferenceTransform'},\n", + " 'uncertainty_samples': 100,\n", + " 'weekly_seasonality': 'auto',\n", + " 'yearly_seasonality': 'auto'}],\n", + " 'threshold': {'abs_score': True,\n", + " 'alm_suppress_minutes': 120,\n", + " 'alm_threshold': 4,\n", + " 'alm_window_minutes': 60,\n", + " 'min_alm_in_window': 2,\n", + " 'name': 'AggregateAlarms'},\n", + " 'transform': {'name': 'Identity'}}\n" + ] + } + ], + "source": [ + "# Save the ensemble\n", + "path = os.path.join(\"models\", \"ensemble\")\n", + "ensemble.save(path)\n", + "\n", + "# Print the config saved. Note that we've saved all individual models,\n", + "# and their paths are specified under the model_paths key.\n", + "pp = pprint.PrettyPrinter()\n", + "with open(os.path.join(path, \"config.json\")) as f:\n", + " print(f\"Ensemble Config\")\n", + " pp.pprint(json.load(f))\n", + "\n", + "# Load the selector\n", + "selector_loaded = DetectorEnsemble.load(dirname=path)\n", + "\n", + "# Load the selector using the ModelFactory\n", + "selector_factory_loaded = ModelFactory.load(name=\"DetectorEnsemble\", model_path=path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating Live Model Deployment\n", + "\n", + "A typical model deployment scenario is as follows:\n", + "1. Train an initial model on some recent historical data, optionally with labels.\n", + "1. At a regular interval `retrain_freq` (e.g. once per week), retrain the entire model unsupervised (i.e. with no labels) on the most recent data.\n", + "1. Obtain the model's predicted anomaly scores for the time series values that occur between re-trainings. We perform this operation in batch, but a deployment scenario may do this in streaming.\n", + "1. Optionally, specify a maximum amount of data (`train_window`) that the model should use for training (e.g. the most recent 2 weeks of data).\n", + "\n", + "We provide a `TSADEvaluator` object which simulates the above deployment scenario, and also allows a user to evaluate the quality of the forecaster according to an evaluation metric of their choice. We illustrate an example below using the ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the evaluator\n", + "from merlion.evaluate.anomaly import TSADEvaluator, TSADEvaluatorConfig\n", + "\n", + "evaluator = TSADEvaluator(model=ensemble, config=TSADEvaluatorConfig(retrain_freq=\"7d\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:22:42 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:22:43 - cmdstanpy - INFO - Chain [1] done processing\n", + "TSADEvaluator: 10%|█ | 604800/5783700 [00:00<00:03, 1307785.29it/s]17:22:44 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:22:44 - cmdstanpy - INFO - Chain [1] done processing\n", + "TSADEvaluator: 21%|██ | 1209600/5783700 [00:02<00:08, 551941.29it/s]17:22:46 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:22:46 - cmdstanpy - INFO - Chain [1] done processing\n", + "TSADEvaluator: 31%|███▏ | 1814400/5783700 [00:04<00:10, 365146.12it/s]17:22:48 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:22:48 - cmdstanpy - INFO - Chain [1] done processing\n", + "TSADEvaluator: 42%|████▏ | 2419200/5783700 [00:06<00:09, 339456.87it/s]17:22:50 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:22:51 - cmdstanpy - INFO - Chain [1] done processing\n", + "TSADEvaluator: 52%|█████▏ | 3024000/5783700 [00:08<00:09, 291111.79it/s]17:22:53 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:22:53 - cmdstanpy - INFO - Chain [1] done processing\n", + "TSADEvaluator: 63%|██████▎ | 3628800/5783700 [00:11<00:07, 269495.41it/s]17:22:55 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:22:58 - cmdstanpy - INFO - Chain [1] done processing\n", + "TSADEvaluator: 73%|███████▎ | 4233600/5783700 [00:15<00:07, 206629.47it/s]17:23:00 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:23:01 - cmdstanpy - INFO - Chain [1] done processing\n", + "TSADEvaluator: 84%|████████▎ | 4838400/5783700 [00:19<00:05, 188649.53it/s]17:23:04 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:23:06 - cmdstanpy - INFO - Chain [1] done processing\n", + "TSADEvaluator: 94%|█████████▍| 5443200/5783700 [00:24<00:02, 166964.38it/s]17:23:09 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:23:12 - cmdstanpy - INFO - Chain [1] done processing\n", + "TSADEvaluator: 100%|██████████| 5783700/5783700 [00:29<00:00, 193604.33it/s]\n" + ] + } + ], + "source": [ + "# The kwargs we would provide to ensemble.train() for the initial training\n", + "# Note that we are training the ensemble unsupervised.\n", + "train_kwargs = {\"anomaly_labels\": None}\n", + "\n", + "# We will use the default kwargs for re-training (these leave the\n", + "# post-rules unchanged, since there are no new labels)\n", + "retrain_kwargs = None\n", + "\n", + "# We call evaluator.get_predict() to get the time series of anomaly scores\n", + "# produced by the anomaly detector when deployed in this manner\n", + "train_scores, test_scores = evaluator.get_predict(\n", + " train_vals=train_data, test_vals=test_data,\n", + " train_kwargs=train_kwargs, retrain_kwargs=retrain_kwargs\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ensemble Performance\n", + "Precision: 0.5000\n", + "Recall: 0.6667\n", + "F1: 0.5714\n", + "MTTD: 1 days 10:25:00\n", + "\n" + ] + } + ], + "source": [ + "# Now let's evaluate how we did.\n", + "precision = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.Precision)\n", + "recall = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.Recall)\n", + "f1 = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.F1)\n", + "mttd = evaluator.evaluate(ground_truth=test_labels, predict=test_scores, metric=TSADMetric.MeanTimeToDetect)\n", + "print(\"Ensemble Performance\")\n", + "print(f\"Precision: {precision:.4f}\")\n", + "print(f\"Recall: {recall:.4f}\")\n", + "print(f\"F1: {f1:.4f}\")\n", + "print(f\"MTTD: {mttd}\")\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, we see that by simply re-training the ensemble weekly in an unsupervised manner, we have increased the precision from $\\frac{2}{5}$ to $\\frac{2}{4}$, while leaving unchanged the recall and mean time to detect. This is due to data drift over time." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/v2.0.2/tutorials/anomaly/2_AnomalyMultivariate.html b/v2.0.2/tutorials/anomaly/2_AnomalyMultivariate.html new file mode 100644 index 000000000..09356b88c --- /dev/null +++ b/v2.0.2/tutorials/anomaly/2_AnomalyMultivariate.html @@ -0,0 +1,781 @@ + + + + + + Multivariate Time Series Anomaly Detection — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

Multivariate Time Series Anomaly Detection

+

Multivariate time series anomaly detection works in largely the same way as univariate time series anomaly detection (covered here and here). To begin, we will load the multivariate MSL dataset for time series anomaly detection.

+
+
[1]:
+
+
+
from merlion.utils import TimeSeries
+from ts_datasets.anomaly import MSL
+
+time_series, metadata = MSL()[0]
+train_data = TimeSeries.from_pd(time_series[metadata.trainval])
+test_data = TimeSeries.from_pd(time_series[~metadata.trainval])
+test_labels = TimeSeries.from_pd(metadata.anomaly[~metadata.trainval])
+
+print(f"Time series is {train_data.dim}-dimensional")
+
+
+
+
+
+
+
+
+Time series is 55-dimensional
+
+
+
+

Model Initialization and Training

+

For the purposes of this tutorial, we will be using 3 models:

+
    +

  1. DefaultDetector (which automatically detects whether the input time series is univariate or multivariate);

    2. +

  2. IsolationForest (a classic algorithm); and

    3. +

  3. A DetectorEnsemble which takes the maximum anomaly score returned by either model.

    4. +
+

Note that while all multivariate anomaly detection models can be used on univariate time series, some Merlion models (e.g. WindStats, ZMS, StatThreshold) are specific to univariate time series. However, the API is identical to that of univariate anomaly detection models.

+
+
[2]:
+
+
+
# We initialize models using the model factory in this tutorial
+# We manually set the detection threshold to 2 (in standard deviation units) for all models
+from merlion.models.factory import ModelFactory
+from merlion.post_process.threshold import AggregateAlarms
+
+model1 = ModelFactory.create("DefaultDetector",
+                             threshold=AggregateAlarms(alm_threshold=2))
+
+model2 = ModelFactory.create("IsolationForest",
+                             threshold=AggregateAlarms(alm_threshold=2))
+
+# Here, we create a _max ensemble_ that takes the maximal anomaly score
+# returned by any individual model (rather than the mean).
+model3 = ModelFactory.create("DetectorEnsemble", models=[model1, model2],
+                             threshold=AggregateAlarms(alm_threshold=2),
+                             combiner={"name": "Max"})
+
+for model in [model1, model2, model3]:
+    print(f"Training {type(model).__name__}...")
+    train_scores = model.train(train_data)
+
+
+
+
+
+
+
+
+Training DefaultDetector...
+ |████████████████████████████████████████| 100.0% Complete, Loss 0.5998
+Training IsolationForest...
+Training DetectorEnsemble...
+ |████████████████████████████████████████| 100.0% Complete, Loss 0.6001
+
+
+
+
+

Model Inference and Quantitative Evaluation

+

Like univariate models, we may call get_anomaly_label() to get a sequence of post-processed (calibrated and thresholded) training scores. We can then use these to evaluate the model’s performance.

+
+
[3]:
+
+
+
from merlion.evaluate.anomaly import TSADMetric
+
+for model in [model1, model2, model3]:
+    labels = model.get_anomaly_label(test_data)
+    precision = TSADMetric.PointAdjustedPrecision.value(ground_truth=test_labels, predict=labels)
+    recall = TSADMetric.PointAdjustedRecall.value(ground_truth=test_labels, predict=labels)
+    f1 = TSADMetric.PointAdjustedF1.value(ground_truth=test_labels, predict=labels)
+    mttd = TSADMetric.MeanTimeToDetect.value(ground_truth=test_labels, predict=labels)
+    print(f"{type(model).__name__}")
+    print(f"Precision: {precision:.4f}")
+    print(f"Recall:    {recall:.4f}")
+    print(f"F1:        {f1:.4f}")
+    print(f"MTTD:      {mttd}")
+    print()
+
+
+
+
+
+
+
+
+DefaultDetector
+Precision: 0.9659
+Recall:    0.8312
+F1:        0.8935
+MTTD:      0 days 01:21:15
+
+IsolationForest
+Precision: 0.9638
+Recall:    0.8192
+F1:        0.8856
+MTTD:      0 days 01:40:57
+
+DetectorEnsemble
+Precision: 0.9620
+Recall:    0.8184
+F1:        0.8844
+MTTD:      0 days 01:34:51
+
+
+
+

We can also use a TSADEvaluator to evaluate a model in a manner that simulates live deployment. Here, we train an initial model on the training data, and we obtain its predictions on the training data using a sliding window of 1 week (cadence="1w"). However, we only retrain the model every 4 weeks (retrain_freq="4w").

+
+
[4]:
+
+
+
from merlion.evaluate.anomaly import TSADEvaluator, TSADEvaluatorConfig
+for model in [model1, model2, model3]:
+    print(f"{type(model).__name__} Sliding Window Evaluation")
+    evaluator = TSADEvaluator(model=model, config=TSADEvaluatorConfig(
+        cadence="1w", retrain_freq="4w"))
+    train_result, test_pred = evaluator.get_predict(train_vals=train_data, test_vals=test_data)
+    precision = evaluator.evaluate(ground_truth=test_labels, predict=test_pred,
+                                   metric=TSADMetric.PointAdjustedPrecision)
+    recall = evaluator.evaluate(ground_truth=test_labels, predict=test_pred,
+                                metric=TSADMetric.PointAdjustedRecall)
+    f1 = evaluator.evaluate(ground_truth=test_labels, predict=test_pred,
+                            metric=TSADMetric.PointAdjustedF1)
+    mttd = evaluator.evaluate(ground_truth=test_labels, predict=test_pred,
+                              metric=TSADMetric.MeanTimeToDetect)
+    print(f"Precision: {precision:.4f}")
+    print(f"Recall:    {recall:.4f}")
+    print(f"F1:        {f1:.4f}")
+    print(f"MTTD:      {mttd}")
+    print()
+
+
+
+
+
+
+
+
+DefaultDetector Sliding Window Evaluation
+ |████████████████████████████████████████| 100.0% Complete, Loss 0.5998
+
+
+
+
+
+
+
+TSADEvaluator:  55%|█████▍    | 2419200/4423680 [00:23<00:19, 101454.83it/s]
+
+
+
+
+
+
+
+ |████████████████████████████████████████| 100.0% Complete, Loss 0.6667
+
+
+
+
+
+
+
+TSADEvaluator: 100%|██████████| 4423680/4423680 [02:09<00:00, 34157.67it/s]
+
+
+
+
+
+
+
+Precision: 0.9522
+Recall:    0.8027
+F1:        0.8711
+MTTD:      0 days 01:22:46
+
+IsolationForest Sliding Window Evaluation
+
+
+
+
+
+
+
+TSADEvaluator: 100%|██████████| 4423680/4423680 [00:27<00:00, 160149.84it/s]
+
+
+
+
+
+
+
+Precision: 0.9666
+Recall:    0.8321
+F1:        0.8943
+MTTD:      0 days 01:40:42
+
+DetectorEnsemble Sliding Window Evaluation
+ |████████████████████████████████████████| 100.0% Complete, Loss 0.6002
+
+
+
+
+
+
+
+TSADEvaluator:  55%|█████▍    | 2419200/4423680 [00:28<00:24, 83276.94it/s]
+
+
+
+
+
+
+
+ |████████████████████████████████████████| 100.0% Complete, Loss 0.6532
+
+
+
+
+
+
+
+TSADEvaluator: 100%|██████████| 4423680/4423680 [02:37<00:00, 28002.66it/s]
+
+
+
+
+
+
+
+Precision: 0.9453
+Recall:    0.8209
+F1:        0.8787
+MTTD:      0 days 01:32:18
+
+
+
+
+
+ + +
+
+ +
+
+
+
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+ +
+
+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/anomaly/2_AnomalyMultivariate.ipynb b/v2.0.2/tutorials/anomaly/2_AnomalyMultivariate.ipynb new file mode 100644 index 000000000..290a4ae6c --- /dev/null +++ b/v2.0.2/tutorials/anomaly/2_AnomalyMultivariate.ipynb @@ -0,0 +1,306 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "08390475", + "metadata": {}, + "source": [ + "# Multivariate Time Series Anomaly Detection\n", + "\n", + "Multivariate time series anomaly detection works in largely the same way as univariate time series anomaly detection (covered [here](0_AnomalyIntro.ipynb) and [here](1_AnomalyFeatures.ipynb)). To begin, we will load the multivariate `MSL` dataset for time series anomaly detection." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "894a554f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time series is 55-dimensional\n" + ] + } + ], + "source": [ + "from merlion.utils import TimeSeries\n", + "from ts_datasets.anomaly import MSL\n", + "\n", + "time_series, metadata = MSL()[0]\n", + "train_data = TimeSeries.from_pd(time_series[metadata.trainval])\n", + "test_data = TimeSeries.from_pd(time_series[~metadata.trainval])\n", + "test_labels = TimeSeries.from_pd(metadata.anomaly[~metadata.trainval])\n", + "\n", + "print(f\"Time series is {train_data.dim}-dimensional\")" + ] + }, + { + "cell_type": "markdown", + "id": "5b9174b4", + "metadata": {}, + "source": [ + "## Model Initialization and Training\n", + "\n", + "For the purposes of this tutorial, we will be using 3 models:\n", + "\n", + "1. `DefaultDetector` (which automatically detects whether the input time series is univariate or multivariate);\n", + "2. `IsolationForest` (a classic algorithm); and \n", + "3. A `DetectorEnsemble` which takes the maximum anomaly score returned by either model.\n", + "\n", + "Note that while all multivariate anomaly detection models can be used on univariate time series, some Merlion models (e.g. `WindStats`, `ZMS`, `StatThreshold`) are specific to univariate time series. However, the API is identical to that of univariate anomaly detection models." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cc5c6e4b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training DefaultDetector...\n", + " |████████████████████████████████████████| 100.0% Complete, Loss 0.5998\n", + "Training IsolationForest...\n", + "Training DetectorEnsemble...\n", + " |████████████████████████████████████████| 100.0% Complete, Loss 0.6001\n" + ] + } + ], + "source": [ + "# We initialize models using the model factory in this tutorial\n", + "# We manually set the detection threshold to 2 (in standard deviation units) for all models\n", + "from merlion.models.factory import ModelFactory\n", + "from merlion.post_process.threshold import AggregateAlarms\n", + "\n", + "model1 = ModelFactory.create(\"DefaultDetector\",\n", + " threshold=AggregateAlarms(alm_threshold=2))\n", + "\n", + "model2 = ModelFactory.create(\"IsolationForest\",\n", + " threshold=AggregateAlarms(alm_threshold=2))\n", + "\n", + "# Here, we create a _max ensemble_ that takes the maximal anomaly score\n", + "# returned by any individual model (rather than the mean).\n", + "model3 = ModelFactory.create(\"DetectorEnsemble\", models=[model1, model2],\n", + " threshold=AggregateAlarms(alm_threshold=2),\n", + " combiner={\"name\": \"Max\"})\n", + "\n", + "for model in [model1, model2, model3]:\n", + " print(f\"Training {type(model).__name__}...\")\n", + " train_scores = model.train(train_data)" + ] + }, + { + "cell_type": "markdown", + "id": "15cb56f7", + "metadata": {}, + "source": [ + "## Model Inference and Quantitative Evaluation\n", + "\n", + "Like univariate models, we may call `get_anomaly_label()` to get a sequence of post-processed (calibrated and thresholded) training scores. We can then use these to evaluate the model's performance." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e18c0534", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DefaultDetector\n", + "Precision: 0.9659\n", + "Recall: 0.8312\n", + "F1: 0.8935\n", + "MTTD: 0 days 01:21:15\n", + "\n", + "IsolationForest\n", + "Precision: 0.9638\n", + "Recall: 0.8192\n", + "F1: 0.8856\n", + "MTTD: 0 days 01:40:57\n", + "\n", + "DetectorEnsemble\n", + "Precision: 0.9620\n", + "Recall: 0.8184\n", + "F1: 0.8844\n", + "MTTD: 0 days 01:34:51\n", + "\n" + ] + } + ], + "source": [ + "from merlion.evaluate.anomaly import TSADMetric\n", + "\n", + "for model in [model1, model2, model3]:\n", + " labels = model.get_anomaly_label(test_data)\n", + " precision = TSADMetric.PointAdjustedPrecision.value(ground_truth=test_labels, predict=labels)\n", + " recall = TSADMetric.PointAdjustedRecall.value(ground_truth=test_labels, predict=labels)\n", + " f1 = TSADMetric.PointAdjustedF1.value(ground_truth=test_labels, predict=labels)\n", + " mttd = TSADMetric.MeanTimeToDetect.value(ground_truth=test_labels, predict=labels)\n", + " print(f\"{type(model).__name__}\")\n", + " print(f\"Precision: {precision:.4f}\")\n", + " print(f\"Recall: {recall:.4f}\")\n", + " print(f\"F1: {f1:.4f}\")\n", + " print(f\"MTTD: {mttd}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "id": "2972aa3e", + "metadata": {}, + "source": [ + "We can also use a `TSADEvaluator` to evaluate a model in a manner that simulates live deployment. Here, we train an initial model on the training data, and we obtain its predictions on the training data using a sliding window of 1 week (`cadence=\"1w\"`). However, we only retrain the model every 4 weeks (`retrain_freq=\"4w\"`)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "47a4508d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DefaultDetector Sliding Window Evaluation\n", + " |████████████████████████████████████████| 100.0% Complete, Loss 0.5998\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "TSADEvaluator: 55%|█████▍ | 2419200/4423680 [00:23<00:19, 101454.83it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " |████████████████████████████████████████| 100.0% Complete, Loss 0.6667\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "TSADEvaluator: 100%|██████████| 4423680/4423680 [02:09<00:00, 34157.67it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Precision: 0.9522\n", + "Recall: 0.8027\n", + "F1: 0.8711\n", + "MTTD: 0 days 01:22:46\n", + "\n", + "IsolationForest Sliding Window Evaluation\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "TSADEvaluator: 100%|██████████| 4423680/4423680 [00:27<00:00, 160149.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Precision: 0.9666\n", + "Recall: 0.8321\n", + "F1: 0.8943\n", + "MTTD: 0 days 01:40:42\n", + "\n", + "DetectorEnsemble Sliding Window Evaluation\n", + " |████████████████████████████████████████| 100.0% Complete, Loss 0.6002\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "TSADEvaluator: 55%|█████▍ | 2419200/4423680 [00:28<00:24, 83276.94it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " |████████████████████████████████████████| 100.0% Complete, Loss 0.6532\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "TSADEvaluator: 100%|██████████| 4423680/4423680 [02:37<00:00, 28002.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Precision: 0.9453\n", + "Recall: 0.8209\n", + "F1: 0.8787\n", + "MTTD: 0 days 01:32:18\n", + "\n" + ] + } + ], + "source": [ + "from merlion.evaluate.anomaly import TSADEvaluator, TSADEvaluatorConfig\n", + "for model in [model1, model2, model3]:\n", + " print(f\"{type(model).__name__} Sliding Window Evaluation\")\n", + " evaluator = TSADEvaluator(model=model, config=TSADEvaluatorConfig(\n", + " cadence=\"1w\", retrain_freq=\"4w\"))\n", + " train_result, test_pred = evaluator.get_predict(train_vals=train_data, test_vals=test_data)\n", + " precision = evaluator.evaluate(ground_truth=test_labels, predict=test_pred,\n", + " metric=TSADMetric.PointAdjustedPrecision)\n", + " recall = evaluator.evaluate(ground_truth=test_labels, predict=test_pred,\n", + " metric=TSADMetric.PointAdjustedRecall)\n", + " f1 = evaluator.evaluate(ground_truth=test_labels, predict=test_pred,\n", + " metric=TSADMetric.PointAdjustedF1)\n", + " mttd = evaluator.evaluate(ground_truth=test_labels, predict=test_pred,\n", + " metric=TSADMetric.MeanTimeToDetect)\n", + " print(f\"Precision: {precision:.4f}\")\n", + " print(f\"Recall: {recall:.4f}\")\n", + " print(f\"F1: {f1:.4f}\")\n", + " print(f\"MTTD: {mttd}\")\n", + " print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/v2.0.2/tutorials/anomaly/3_AnomalyNewModel.html b/v2.0.2/tutorials/anomaly/3_AnomalyNewModel.html new file mode 100644 index 000000000..259543efc --- /dev/null +++ b/v2.0.2/tutorials/anomaly/3_AnomalyNewModel.html @@ -0,0 +1,923 @@ + + + + + + Adding New Anomaly Detection Models — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

Adding New Anomaly Detection Models

+

This notebook provides a minimal example on how to add a new anomaly detection model to Merlion. We follow the instructions in CONTRIBUTING.md. We suggest you review this notebook explaining how to use a Merlion anomaly detection model before reading this notebook.

+

More specifically, let’s implement an anomaly detection model whose anomaly score is just equal to the value of the time series metric (after mean and variance normalization). By default, we want the model to fire an alarm when the value of the time series metric is more than 3 standard deviations away from the mean value.

+

Note that this is already implemented here as the StatThreshold algorithm. For a more complete example, see our implementation of Isolation Forest.

+
+

Model Config Class

+

The first step of creating a new model is defining an appropriate config class, which inherits from DetectorConfig:

+
+
[1]:
+
+
+
from merlion.models.anomaly.base import DetectorConfig
+from merlion.post_process.threshold import Threshold
+from merlion.transform.normalize import MeanVarNormalize
+
+class StatThresholdConfig(DetectorConfig):
+    # If the transform argument is not provided when initializing the config,
+    # we will pre-process the input by normalizing it to be zero mean (but
+    # not unit variance!)
+    _default_transform = MeanVarNormalize(normalize_scale=False)
+
+    # When you call model.get_anomaly_label(), you will transform the model's
+    # raw anomaly scores (returned by model.get_anomaly_score() into z-scores,
+    # and you will apply a thresholding rule to suppress all anomaly scores
+    # with magnitude smaller than the threshold. Here, we only wish to report
+    # 3-sigma events.
+    _default_threshold = Threshold(alm_threshold=3.0)
+
+    def __init__(self, **kwargs):
+        """
+        Provide model-specific config parameters here, with kwargs to capture any
+        general-purpose arguments used by the base class. For DetectorConfig,
+        these are transform and post_rule.
+
+        We include the initializer here for clarity. In this case, it may be
+        excluded, as it only calls the superclass initializer.
+        """
+        super().__init__(**kwargs)
+
+
+
+
+
+

Model Class

+

Next, we define the model itself, which must inherit from the DetectorBase base class and define all abstract methods. See the API docs for more details.

+
+
[2]:
+
+
+
import pandas as pd
+
+from merlion.evaluate.anomaly import TSADMetric
+from merlion.models.anomaly.base import DetectorBase
+from merlion.utils import TimeSeries
+
+
+class StatThreshold(DetectorBase):
+    # The config class for StatThreshold is StatThresholdConfig, defined above
+    config_class = StatThresholdConfig
+
+    # By default, we would like to train the model's post-rule (i.e. the threshold
+    # at which we fire an alert) to maximize F1 score
+    _default_post_rule_train_config = dict(metric=TSADMetric.F1)
+
+    @property
+    def require_even_sampling(self) -> bool:
+        """
+        Many models assume the input time series is sampled evenly.
+        That isn't a necessary assumption for this model, so override the property.
+        """
+        return False
+
+    @property
+    def require_univariate(self) -> bool:
+        """
+        Specify that this model only works for univariate data.
+        """
+        return True
+
+    def __init__(self, config: StatThresholdConfig):
+        """
+        Sets the model config and any other local variables. We include the
+        initializer here for clarity. In this case, it may be excluded, as it
+        only calls the superclass initializer.
+
+        :param config: model configuration
+        """
+        super().__init__(config)
+
+    def _train(self, train_data: pd.DataFrame, train_config=None) -> pd.DataFrame:
+        # Since this model's anomaly scores are just the values of the time
+        # series metric, there is no model to train.
+        # In general, you should train your model here, and determine its
+        # anomaly scores on the training data.
+        return train_data
+
+    def _get_anomaly_score(self, time_series: pd.DataFrame,
+                          time_series_prev: pd.DataFrame = None) -> pd.DataFrame:
+        # The time series values (assuming a univariate time series) are the anomaly scores.
+        return time_series
+
+
+
+
+
+

Running the Model: A Simple Example

+

Let’s try running this model on some actual data! This next part assumes you’ve installed ts_datasets. We’ll begin by getting a time series from the NAB dataset & visualizing it.

+
+
[3]:
+
+
+
import matplotlib.pyplot as plt
+import numpy as np
+
+from merlion.plot import plot_anoms
+from merlion.utils import TimeSeries
+from ts_datasets.anomaly import NAB
+
+# This is a time series with anomalies in both the train and test split.
+# time_series and metadata are both time-indexed pandas DataFrames.
+time_series, metadata = NAB(subset="realKnownCause")[3]
+
+# Visualize the full time series
+fig = plt.figure(figsize=(10, 6))
+ax = fig.add_subplot(111)
+ax.plot(time_series)
+
+# Label the train/test split with a dashed line & plot anomalies
+ax.axvline(metadata[metadata.trainval].index[-1], ls="--", lw=2, c="k")
+plot_anoms(ax, TimeSeries.from_pd(metadata.anomaly))
+
+
+
+
+
+
+
+
+Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/ec2_request_latency_system_failure.csv (index 2) has timestamp duplicates. Kept first values.
+Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/machine_temperature_system_failure.csv (index 3) has timestamp duplicates. Kept first values.
+
+
+
+
[3]:
+
+
+
+
+<AxesSubplot:>
+
+
+
+
+
+
+../../_images/tutorials_anomaly_3_AnomalyNewModel_6_2.png +
+
+

Now, we’ll split the data into train & test splits, and run our anomaly detection model on it.

+
+
[4]:
+
+
+
# Get training split
+train = time_series[metadata.trainval]
+train_data = TimeSeries.from_pd(train)
+train_labels = TimeSeries.from_pd(metadata[metadata.trainval].anomaly)
+
+# Get testing split
+test = time_series[~metadata.trainval]
+test_data = TimeSeries.from_pd(test)
+test_labels = TimeSeries.from_pd(metadata[~metadata.trainval].anomaly)
+
+
+
+
+
[5]:
+
+
+
# Initialize a model & train it. The dataframe returned & printed
+# below is the model's anomaly scores on the training data.
+model = StatThreshold(StatThresholdConfig())
+model.train(train_data=train_data, anomaly_labels=train_labels)
+
+
+
+
+
[5]:
+
+
+
+
+                     anom_score
+timestamp
+2013-12-02 21:15:00   -9.065700
+2013-12-02 21:20:00   -8.097140
+2013-12-02 21:25:00   -6.908860
+2013-12-02 21:30:00   -4.892315
+2013-12-02 21:35:00   -3.703186
+...                         ...
+2013-12-14 16:25:00   15.041999
+2013-12-14 16:30:00   14.494303
+2013-12-14 16:35:00   16.234568
+2013-12-14 16:40:00   15.160902
+2013-12-14 16:45:00   15.055357
+
+[3403 rows x 1 columns]
+
+
+
+
[6]:
+
+
+
# Let's run the our model on the test data, both with and without
+# applying the post-rule. Notice that applying the post-rule filters out
+# a lot of entries!
+import pandas as pd
+anom_scores = model.get_anomaly_score(test_data).to_pd()
+anom_labels = model.get_anomaly_label(test_data).to_pd()
+print(pd.DataFrame({"no post rule": anom_scores.iloc[:, 0],
+                    "with post rule": anom_labels.iloc[:, 0]}))
+
+
+
+
+
+
+
+
+                     no post rule  with post rule
+2013-12-14 16:50:00     14.520900             0.0
+2013-12-14 16:55:00     15.065935             0.0
+2013-12-14 17:00:00     15.825172             0.0
+2013-12-14 17:05:00     13.846644             0.0
+2013-12-14 17:10:00     15.050966             0.0
+...                           ...             ...
+2014-02-19 15:05:00     15.152393             0.0
+2014-02-19 15:10:00     14.771146             0.0
+2014-02-19 15:15:00     14.102446             0.0
+2014-02-19 15:20:00     15.023830             0.0
+2014-02-19 15:25:00     13.870839             0.0
+
+[19280 rows x 2 columns]
+
+
+
+
[7]:
+
+
+
# Additionally, notice that the nonzero post-processed anomaly scores,
+# are interpretable as z-scores. This is due to the automatic calibration.
+print(anom_labels[anom_labels.iloc[:, 0] != 0])
+
+
+
+
+
+
+
+
+                     anom_score
+time
+2013-12-16 06:40:00   -3.024196
+2013-12-16 06:45:00   -3.012073
+2013-12-16 07:00:00   -3.468464
+2013-12-16 07:05:00   -3.124039
+2013-12-16 07:10:00   -3.491421
+...                         ...
+2014-02-09 11:35:00   -4.819248
+2014-02-09 11:40:00   -4.823173
+2014-02-09 11:45:00   -4.822201
+2014-02-09 11:50:00   -4.677379
+2014-02-09 11:55:00   -4.300280
+
+[721 rows x 1 columns]
+
+
+
+
+

Visualization

+

Qualitatively, we can plot the anomaly score sequences to see the difference.

+
+
[8]:
+
+
+
print("no post rule")
+fig, ax = model.plot_anomaly(test_data, filter_scores=False)
+plot_anoms(ax, test_labels)
+plt.show()
+
+
+
+
+
+
+
+
+no post rule
+
+
+
+
+
+
+../../_images/tutorials_anomaly_3_AnomalyNewModel_13_1.png +
+
+
+
[9]:
+
+
+
print("with post rule")
+fig, ax = model.plot_anomaly(test_data, filter_scores=True)
+plot_anoms(ax, test_labels)
+plt.show()
+
+
+
+
+
+
+
+
+with post rule
+
+
+
+
+
+
+../../_images/tutorials_anomaly_3_AnomalyNewModel_14_1.png +
+
+
+
+

Customizing the Post-Rule

+

The example above uses a simple threshold as the post-processing rule. As a result, the model is continuously firing alerts when the differenced time series value is far from 0. We support an alternative option which combines successive alerts into a single alert. We demonstrate this below.

+
+
[10]:
+
+
+
from merlion.post_process.threshold import AggregateAlarms
+
+# We use a custom post-rule (AggegateAlarms instead of the default Threshold),
+# where we suppress all alarms for a day after the most recent one is fired.
+threshold = AggregateAlarms(alm_threshold=3.0, alm_suppress_minutes=24*60)
+model2 = StatThreshold(StatThresholdConfig(threshold=threshold))
+
+# Train the model as before
+model2.train(train_data, train_labels)
+
+# Visualize the model's anomaly scores with the new post-rule
+fig, ax = model2.plot_anomaly(test_data, filter_scores=True)
+plot_anoms(ax, test_labels)
+plt.show()
+
+
+
+
+
+
+
+../../_images/tutorials_anomaly_3_AnomalyNewModel_16_0.png +
+
+
+
+

Quantitative Evaluation

+

Finally, you may quantitatively evaluate the performance of your model as well. Here, we compute precision, recall, and F1 score for model2 above.

+
+
[11]:
+
+
+
anom_labels2 = model2.get_anomaly_label(test_data)
+
+print("Model Evaluation")
+print(f"Precision: {TSADMetric.Precision.value(ground_truth=test_labels, predict=anom_labels2):.4f}")
+print(f"Recall:    {TSADMetric.Recall.value(ground_truth=test_labels, predict=anom_labels2):.4f}")
+print(f"F1 Score:  {TSADMetric.F1.value(ground_truth=test_labels, predict=anom_labels2):.4f}")
+
+
+
+
+
+
+
+
+Model Evaluation
+Precision: 0.5000
+Recall:    1.0000
+F1 Score:  0.6667
+
+
+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
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+ +
+
+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/anomaly/3_AnomalyNewModel.ipynb b/v2.0.2/tutorials/anomaly/3_AnomalyNewModel.ipynb new file mode 100644 index 000000000..bec078ac6 --- /dev/null +++ b/v2.0.2/tutorials/anomaly/3_AnomalyNewModel.ipynb @@ -0,0 +1,493 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adding New Anomaly Detection Models\n", + "\n", + "This notebook provides a minimal example on how to add a new anomaly detection model to Merlion. We follow the instructions in [CONTRIBUTING.md](https://github.com/salesforce/Merlion/blob/main/CONTRIBUTING.md). We suggest you review this [notebook](1_AnomalyFeatures.ipynb) explaining how to use a Merlion anomaly detection model before reading this notebook.\n", + "\n", + "More specifically, let's implement an anomaly detection model whose anomaly score is just equal to the value of the time series metric (after mean and variance normalization). By default, we want the model to fire an alarm when the value of the time series metric is more than 3 standard deviations away from the mean value.\n", + "\n", + "Note that this is already implemented [here](https://github.com/salesforce/Merlion/blob/main/merlion/models/anomaly/stat_threshold.py) as the `StatThreshold` algorithm. For a more complete example, see our implementation of [Isolation Forest](https://github.com/salesforce/Merlion/blob/main/merlion/models/anomaly/isolation_forest.py)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Config Class\n", + "\n", + "The first step of creating a new model is defining an appropriate config class, which inherits from `DetectorConfig`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from merlion.models.anomaly.base import DetectorConfig\n", + "from merlion.post_process.threshold import Threshold\n", + "from merlion.transform.normalize import MeanVarNormalize\n", + "\n", + "class StatThresholdConfig(DetectorConfig):\n", + " # If the transform argument is not provided when initializing the config,\n", + " # we will pre-process the input by normalizing it to be zero mean (but\n", + " # not unit variance!)\n", + " _default_transform = MeanVarNormalize(normalize_scale=False)\n", + "\n", + " # When you call model.get_anomaly_label(), you will transform the model's\n", + " # raw anomaly scores (returned by model.get_anomaly_score() into z-scores,\n", + " # and you will apply a thresholding rule to suppress all anomaly scores\n", + " # with magnitude smaller than the threshold. Here, we only wish to report\n", + " # 3-sigma events.\n", + " _default_threshold = Threshold(alm_threshold=3.0)\n", + "\n", + " def __init__(self, **kwargs):\n", + " \"\"\"\n", + " Provide model-specific config parameters here, with kwargs to capture any\n", + " general-purpose arguments used by the base class. For DetectorConfig,\n", + " these are transform and post_rule.\n", + " \n", + " We include the initializer here for clarity. In this case, it may be\n", + " excluded, as it only calls the superclass initializer.\n", + " \"\"\"\n", + " super().__init__(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Class\n", + "\n", + "Next, we define the model itself, which must inherit from the `DetectorBase` base class and define all abstract methods. See the API docs for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "from merlion.evaluate.anomaly import TSADMetric\n", + "from merlion.models.anomaly.base import DetectorBase\n", + "from merlion.utils import TimeSeries\n", + "\n", + "\n", + "class StatThreshold(DetectorBase):\n", + " # The config class for StatThreshold is StatThresholdConfig, defined above\n", + " config_class = StatThresholdConfig\n", + "\n", + " # By default, we would like to train the model's post-rule (i.e. the threshold\n", + " # at which we fire an alert) to maximize F1 score\n", + " _default_post_rule_train_config = dict(metric=TSADMetric.F1)\n", + "\n", + " @property\n", + " def require_even_sampling(self) -> bool:\n", + " \"\"\"\n", + " Many models assume the input time series is sampled evenly.\n", + " That isn't a necessary assumption for this model, so override the property.\n", + " \"\"\"\n", + " return False\n", + "\n", + " @property\n", + " def require_univariate(self) -> bool:\n", + " \"\"\"\n", + " Specify that this model only works for univariate data.\n", + " \"\"\"\n", + " return True\n", + "\n", + " def __init__(self, config: StatThresholdConfig):\n", + " \"\"\"\n", + " Sets the model config and any other local variables. We include the\n", + " initializer here for clarity. In this case, it may be excluded, as it\n", + " only calls the superclass initializer.\n", + " \n", + " :param config: model configuration\n", + " \"\"\"\n", + " super().__init__(config)\n", + "\n", + " def _train(self, train_data: pd.DataFrame, train_config=None) -> pd.DataFrame:\n", + " # Since this model's anomaly scores are just the values of the time\n", + " # series metric, there is no model to train.\n", + " # In general, you should train your model here, and determine its\n", + " # anomaly scores on the training data.\n", + " return train_data\n", + "\n", + " def _get_anomaly_score(self, time_series: pd.DataFrame,\n", + " time_series_prev: pd.DataFrame = None) -> pd.DataFrame:\n", + " # The time series values (assuming a univariate time series) are the anomaly scores.\n", + " return time_series" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the Model: A Simple Example\n", + "\n", + "Let's try running this model on some actual data! This next part assumes you've installed `ts_datasets`. We'll begin by getting a time series from the NAB dataset & visualizing it." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/ec2_request_latency_system_failure.csv (index 2) has timestamp duplicates. Kept first values.\n", + "Time series /Users/abhatnagar/Desktop/Merlion/data/nab/realKnownCause/machine_temperature_system_failure.csv (index 3) has timestamp duplicates. Kept first values.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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mnG67EKxeqBmP6TedgMMPqMw4l5t7ZuS1Oevwp3cX4ZpnoqHLIcEqjxECy+fbrDv6/LXbsXRTelIS2XLdYEx/oBSspGZ80ZSpsTo8pCSKN/0rnQn49Tnr8K0/+S8bocrKzAG0NzhzTvp7PX7wtzpdYslcEpOEvepBXUJpQ1A4zRV177uLcOur83D1375MbfvjO+m8bVZBB14Z0sNDXSkikgzo0i71umv78ozPnfhPOvUDdeKvlWv+dvURyu3GFD0AcJYiN1Z5aQzHDddH+jdrk0CnijIM6t4er/zw6IzjxKK1ECDBKo8RAstXNs/j2Q9/glP+ki5mbDdBPX1Vdca2ObefhmuPHZx6rzqDfNpTe6QndCEAlrm0of/hvNH2Ozng3QV6jdGaAPxhrlc4csYTPEPgWqGZFbKl7rdDNqP2lyYMr7Uiw8Rphmphhv1osTo/XjaypCfIeb1gkFNnqPIvfb1ePeD+5/pjU6+d5opboNC8Z6OYvRvMTHKqxaSVANmvMp2aRGisyjXzSizGUPebU3T7qwS3fIUEqzym0YESRFWhvcGmEO+JB+lNRqeO7IVYjOEnUtFk1Twidzu5fqEQAMtcpFsA9DW0ooZqELj6mEEZKSWE+jysUGKREdpIFFfKdsx3Yf6VMU5UVx092GRP77w9f0Pg5yTCQX5eVm91vggb3b9z6vUjHyx1dEyTwsk6m1n8nSyoDujWzvLzfpVtcfohyZzgVoLV9ScNS70WglVpLC1ydO/QBoO6tcP4IUm/3kMC1CSP03yFw4IEqzxlp8EjfPlujjHT4xgzPY67F6cf9kWKEFa7cjZyuZOPf3Eiaq+oAgB0rChDwz0TMKA8rtRYyfOXPDaIDPFmpWrMkDth1FDlqbrsyAPw9FV6NXpJjgWrF79chUG3vJmaHIJOTREmt72+wNNxxqirYT07uDr+za/WZy07NhE9nCboF8IFAPTuVAEAqDl+CABgp8OFi9w/f3XWCADBa7fLJKfzEbe9oxyL1jalBcg+2ndR8cmSzeCco1zTalmNL7LfobhkzOCHOv3mE/Hz0w4CAFw2ZSbOfPBjmLG/NY7X56zN0AaqAgVuO3uk6XlyQXRnLsISYxT6+V+mN7y4jqcm1iue+kK33ypDpIgdA7q2y6grx6A2Bcrzlyz3ib7nJvM6gNRKJl+oKCvBkYP1bRaCVbaKoBr5pRYtOfjWt3TbjX5F65qsV+ITHvoYr81ORjkGKZyJIAInGCcAJ+0waqc4577v/XXPzcI5j34KQG/eIAqLqt+/hwsf/wwrNjtLyyGnV3n/598CAHRpl+mTZcVbUl40sZDcEXCqgA9v1pcre2PuWsQTHHe/tTCVKmGi5PQds7AsXP7UTMQ5T0WTW/Wt0f3SGrz3vk5qdFV1GGVha+H6Haaa6fMe/Qw/eWEOLnj8M932pr2Zppuw1+S2RZiJaGKnANnUDHRTbD/eoeP25MmTTT+LMfX15U1ydxMaK7d5SnJdKNiuOHGrzQTdqaIso9yOEKzCLtLbrX0b3fsvVmzFof0rlftyzrFg3Q7c+OIcVLYrw1V//RLXnzgMN51+UA5amsaLQGRMhfH2/A04eqiqJ7iHc65b2ROFxZbdzdiyuxl1Dv2jOrQpxY9PGIrR/TqjQ5vkVFrmcvEom91e09K1/Oa1+Rmabz8Yx92fvjgX73+9CW/OW48lm3bh6auOwG4tlUn3DnrBsHPbsoycUBt37E99T6tSTrJbhNHPVcYox23csQ+jJKFMYObbxhShVG6qfGQD0ljlKXYyx+kzMiclYwexio6qqalBTU2N8rMYgIRCZyVrrOK6rNTJ/25NgdnCLLv8HpvcMzv2Wav3VTUMhY9V2Ca5cYOda//ksVKkKXhkmjOfESvc+nV5EaxWGGpg3vjCHDz9aUPq/Ss/Osr1OQV2ZYqmLsycPLLpiPzEh8vwKuXPChwhJNkxsFt7/OKMEThTioyrdKmxGtojaZYe0bsjvlqT1NR88M0mq0Mcc8YhvQEA3Tq0yfhMVBAQ4xI3MdWZBWXEYgxlJcx1Al9VRnqjwOR2Pb1QIXBV+MiJFQTRmOmInGBMmlZ7RWb0n1NU84W8STYFevWxMnLKwb3sd7Lg6ztOBwCccJA6n5NdtOAuG8FKRUnMXmXulnlrtqN+5TY07WnGxh37TAc/OaFm9UB9qoWnPllh3D2FLBQHKRe4NXGoMlK/NW89NioCMsxojifwkJQAtGqgd/Pyy/XWQsw1z9ThkyWbcdTdU7G/NY5Jf6/LMMkGyT1vf4OfvTTXfkfCFTecPEz3Xk78K9OjY6bAcvahmekHrLhDK2h8oxQYFBRd2pcr2yizV3Nm59roLRaCJx6UTJdw6ZEHKI+LsaRWSC6Tc/7YfrZt2rQzs9Bz0x79uPDQ1KUZCxIrp/uOFZmC8KDu4aY/IcFKwZ/e/Qa3vvpV2M2wRJ5zhnl8hvp0NndSrK2tRW1trfKzGFP7WHGeTlaa4MCX2ziqP4xji7ZI8StYefU96KIlsWtXXoqKspgyhBoAnpnRYH19Ra253haOnqu27EkNVBt3mFeOd8OnSzfj2498ggse/wznPPopjvzDVAz51VtKzYicGdzoN2FVvkL2bRrZN63VFP5RTn2kjKza6s6/TyWM/vifs3DZFH8ZpL1iJoweNzxd3+nyp2Zi/fZ9eGf+BkvzBxEuF0+eoUyZAmS6OfzkhTmOz6tKommF0OKu2OyubzihJZ5I+UKZJQeuX7kNa5v2pqIf12njwsOXjcXPTj0QvzxjhPI4BoayWEznaD9aYb4zokp1coQhgm/O6iYs08qGzVuzHWc88BFG3PaO6TlVGrmwIcFKwaPTlnkuGJwr5HnUq5XJyp9o0qRJmDRpkvKzGMx9vISLQZwDT69KoJUDb2/U5zDxyq8nHOzpuHGDu+KgXknzX2ksphMcZIHkuZnWGZBV9ac2WGhPjv/TtJTq+4f/qHfVZjPkVBkrpUCEvyiyh/9NKr0hBrTLFCtQo7Ak+4O1KQtuiFgm1Vi081cD0maKX5yh9+1a5yHBbTZROSzPcuinY8emHfuwcP0OvGKjLQuSl+vX4P2vC1sonLliK/771XrlZ6pn00kZG0Dv2/O/BZkpOPY0typLM40f0hUXVvV3dA2ntMQTKV+oy8erNU8AcMw9H2Rs69CmFDecPNzUVynBOfbHE7qknk4c91XjiUrjxBjDis278e1HPrE1wcd9ZGzPFiRY5SkzpGzr23OcksgsKpBDL1jN0OaW9zcn/2/yoLWRB5vDpNVOWxeleKZ905hKOxFjeo2MU0dVAFizLXNVebPm0D3SpKSJ0+LOTjEzzT1skzdHrKQ7ty2z3A/Q35+/ftLguG12yKv5WTYFYYG0xqpThb7NZkqB+pWZdd1kBtrk5/GKSkshF3xW5ZJT8cmSzRh0y5spAXT7nhaM+8NUnPngx/j5v3Jn8rvpX3Nx7bPRKA2SbXYr/P5UgSa/fcNZqg95sVrz98zF1Mjb38XI29/N2N67c0XguZda4gmUahqrEw4MtpxV/cptGX6jZ47ubXucquzXgb0yfV5LYwz3/W9RxnaZeIJjX0s89MAgFSRY5SlytvVtkiLlz4ekf9L5e8yFD2NaADfEGHekscr4zEOkhll4u1U0ihFZuCmJMZ3g4KT+3I0vzMa5j36qNGVdMLY/Gu6ZgLd+cpzj9vjB6lu3xBOmPhViwBcOrVbI9ydIwVAeAFUTmhHhY9XJgTAI6J3LjxqSGQn40CWHOzqPW1Th6fLK3KgZbti8W/n935ib9OURhX+NGlIzX7qgIhX3NLfinfl6LU5dw1YMuuVNzF3dhLMf/liptc0ntu9pwdJNaaFXVUblgfeti3KP7tc5K3U3K6TKAm+aaNOcctKfp+OteRtSQn0sxvDadcf4OqeMSotU7sDVQzUHlMQYGu6ZoKslm+DW1Qz2tcQx9FdvYcRt7+CMB8xzX4UFCVZZgnOO1S59SoLgZKmUzFVLzRMhvjjJe3QUgz6dgoBD72NlxG1JGwC47kS9I+lTV1bjkL6dTDvd81+swjcbzGv8lMRiOqHMiRbjtTnrMGd1E05XCCV2ubmCznv0rkWG7zXb9tqa2JwUC/ZTDNUKeRKzc6oF0o61Ru2kqrQGANz/XnpCfG7ikRmfH9LX+ruf88gnuMIkU72MUWhTPQHTF6XL6cg+fdMXbcIJ903HIb/N1Fqkz5c848qt+gjHjTvTmq/lkln1mHs+8JyVXmbk7e/ih//Q+x0J/6JzHv0U89fuyAiAccM789fjzAc/Dq0KAQCc99inOOUv6ZQcKrOcHdxyeaOnuTWBO/7zNbbtbsa0Relov937W/HIB3oBTk44ep2J/5cTnmssT5WykiOZnUY7GrEyI8o4SY9jtbiW3TLemrfe1BcWgKXPVRQgwSpLPP/Fahx37zTMyVLG5r7mPtO2ONFaWMFgbpISckar4nMnKxojwswiHIRPPrgXThrRMyUcfbZ0s07rdOur8yxXMJt37cc7knBiNDNZoZoQuts4Topwaid8sSKpHViwznyS/GTpZtPPznjgI2zbY61RcKI1dDLxeak1WN+QNrs6CWR4WIvmm7XKmbl28660qVk1yJfaXHPumu34eIn+/qoE+Ce0SgSpfWxul3w/r/rrl6b7vVSX9KNq0QTbdwxC9Mt1aT8rY76usx/+xLoRHgkyb9f1z83GwvU7Qq1TudyQjuNB7RlzImDJhdSd6t7fnr8eT3+6An94ayGuln77216bj/v+l/aL5Ny8Rp9b/rJevZhz61gvuPPcYGq2AmpToOBmKU9ea5zb+lZFmaISrMTE9bVJFe19LfHA8s7MW9sEAJaTpB+Gtzd/QL/T27oDGScGt5hGBYKnBCuVzqNXJ/fRG7EYw8I7zsAzV4+Trs/AeXKFc9mTM3H6A8kC03bZxAVyLhU5FF/FdGmVabWCMkOVGmBfS1ypYbjy6WSW/DfmrnN9HcBZKQwnY2uryo5r4Ks1211rHuTySk6OFcKziOp0y0UBOAOrTM6d25bhrvNGAQCqBnbBKza5pMR3daod2bM/KXj80xBM8ef3FruunBAlhCnYSz/KFrM1Xz+V35OxCPwXK5ImWjfNF799a4Lr8jMtMwh4Xdu7y3/lBT9JM4cbykBVGdK3XHnUQEfnscrq3kuKsN7fGtcF5+QbRSVYvatFaXyqWPVv39uCEbe9E1jeGfEQZ6vqvZj7BkiLk+Fa2gWVdarhngmpP7+YmQLla6vm5iHd3dVoE7QtL9F1SJHETr61yxt3KXOk2DHVJhmfrGHw4m6k0jCNuO0dnP3wJ/iyQe9sLUxfyzZlltRo2Lxbt2L2iqzJMaYzEP4zTpxBvzt5Bv70rrVzqRVOJtcJWuJFlQnWyL/qMqN4/3TRYY6ee6uKAGYC4PeOHIiGeybglR8dbdu2eILj3QUblJO3QBa6PlrSaLrf8X+alsp9ZMaG7ftwziOfYKciPUgUiKCvsRJjBK38zDpNYim+K2N6gcxYe7LCRTCOFXO3m99ctyXFZIyme2O9vouqB3g+t0C2aDymJSbOV4pKsHpbyzaryrC93caE4hbV5B8kwtQ2RHIRun5w8ufcsM//RTnnpto7Bph6UYvhQSVYBVWhRvQ/edI76c8f4oeKKBwVqvBegVWG9KB9Qz5Zojbrvb9QL+ztb43jhPumY8wd72Uk+vSD0SRTo0WCmeX3MfLRYnMBwA4n91I8fnYmPAC4+WVveefGDKhEpYVGLIjfPM45Hv7AWjMqC107bRLRPv2peXJXABh/91TMXbMdTwcY0Rkk2VpsesVKk/jJL9N19sTz6EZjJYQxY0bzbHHnYvPxy4/Gys7FIIivZ6XNyjeKSrASyc9UYfNlihxLfsyCYtLKVkkLYaJoIz2MIhBpxjbVEcFhqrHiaY2VauwMqvaf6IDG3E1yTil5sOzdqQIXayuqw/p3xtgDzIWTTTvNQ+ODLqTs9HwNUvJAN+kh7DBO4OKZVUVKqTCr3eUEJxoroTmz8w0xmk5n33aq43aUlbAM4UlOBBtEKPdLX6525V945dHOzCp2LNkUTR8VVQmSMFm2abfpZ/27pFeuA7vJmZidjWU8JVg5a4tVmTEnLDH/Kqkiz16w+81yJTjmC0UlWAlUq9B/z84sW+B2tbqnuRX/qluNF79clXJEXdZo8aT7QGiEKiQNsln+zXPG9FV/4BEzHyvxGaB2Xg8K4QP0xIfm6uIWqQGtCZ4SxkpizHJSt/rN5wUQeSXjVLCyitJ784ZjlRFwTjDeh7lrtuPfs3OTiNKJYNWslcuwW2nf8Pxs3Xun6RmA5ISwe39cJ4hf+0w6h1MQ2pXJHy23TGpq9A30WufMuIgzS4Cpwkt0nFc2K+rFhcnyzZmmd5nfaImJRQoNN0+EeHzenmcezdtOSrPw+nXHuji7O4IoTGws0ixwIlcd2t8+M3uhUJSClYr/zs0chNyuVkfe/i5ufvkr/PKVealtcuHZ8x77FINueTMQ88K/1mmreemB3mUyNj7oIX9PVVUVqqrUTu5mUYEc6XXc1zuzJ1mpsowb2d3civ/MXQfOORKcp7QepbGYpXP2hxbmLac16lQZlIf2yKw75LQws1UNQwaGQ/tX6rYN6NoWf7zAPpJHdR9++mI6EeWFVf0D8clTYVVSR/A7G18iM9xMIDNXbMXelrjOFCeclIFgNFaAdYb+ow2Zr71e008UlZn/VxCTsZFsFqb2wu2vWyf/FP5FaVMgd+FjlTxop0Xett9955DU62zcb4GTqEA5l5SKIwcnU40YNWvMgQbvmmMH2+7jlsV3npm1McoPRSlYDVdkelWtoJ1EWS3euNOyOKuox/eb1+alIlBU2jG3zNU0s+83ptu9YX/md/CayG7WrFmYNUvta2PlvC4GnE+sk2BnncufnIn/e342bnllHlrjidSAFYtZa6Vuf30BZi7fovxMVRRYhSqkWHVks8PzWWm2VNr9S444ACP72K8O7Sbw9duDC7U3YqVtFHTTIqWM+XdOOTj45IxmOFkE/fiEoa7Pe5BiDBJ4zSP2wTebAk9lcMX4YMySMlb1NcPALulp2swlOa87PPf+Fvvf8twx9sWLvSLX5nMitN3/3cMsP2/fJqld69pe78zuRB60S03jhai6ZRWlYCWrXgWq1Z6T7Len3f8RbrIoNyHS9f/j83To9N4AB78d0kJI9WN+YBP15oUYc+fAGQYiX82LdauxY19rSjgpNSQIVXFxbbrIbyfJ0d2pWUhVrFklWTk1BVa2NQ/F7lvZFhWGqLb5a7djtIXa/epjBgGwn8B3789evqHTR9pH+l0ybgBKYywjYkoWdrbsCqa4tRlOMvwP7u6+Cno3E5MK4CzdhdlxQZf38Jr7yIrHHQjVYTOgazrcWshVXm6tk2i/cg+Jk53w6o+PxjM/SKepsROsjhve3TZQ5IsVW3H3+aPxwMVjdNudaPAOUxRgNmJVcqtCUWdQ5bf754ushcNcUJSCleoHUiEXjfUKY5kJDrMmZSvOqyq66x9umnldptJbot+sIHIClcQYWuMJXS4rKy4+Ih1GrCppo0LlyClrRIUjs5WmUxYeHpxqbvrsVFGWMRi+rSWWFJnG595+mu7z8Vrm8NY4txQWzxiVFH6+vuN0jBvUFTeeMtx0X7ds2rnf1iT06LRlSkFB3rbRQ/1JO2RhOq4JOcK8a8xtBHgLyrD66l6Fo2WNuzIy7+/c14LHpi/FNo9+TVsDSPFRv3KrLv+WnJU+qtx+dto8J8xc8m9m9ZN3k3JSddCepaOHZpZYyiY3nDQMYw/oohNUrJJzAsDfrzH31RTnadiyB5eOO0CRd8v63OMGd3WU+f3Oc0cptw/o2hbf/D7T7Cfm0kvHpee5CwIuZu2FohSsnEZHuHHoNDMZvLtgA85/7DPdtiUb/QtsKlTfqtKFI6+b65hNDHL3GilZOx4bHNx39lMmpjTGMHfNdoz9/Xu6rNJmjpXyPDXDxERoRBU2LN+ug3qnb4xZmo+ZK7bghS9W4e63FmLHXvvn8JhhmQP3mzcch4Z7JqCzIZ2AqHjfmrDWcNQcNwQA0K68FC/98CiM6O0vYknm75+v9JwzTtbo/O9rc6dgr8h9WWisjh3WHQ33TFAuVMTPfd7h/ZT+HtcqfEusyqLYlSUy44256zJqO47+3f9w7zuLcPjv33N9vt37W/HqLH9uC4s37sQFj8/A8X+altpmTC6ZTRo278Zny8yrFZgxTEqIKeQR8ZvZKTG/Jz0j4rf8bJmzsSMovnVQprncKp3B+YdbmyPttPV2yoIhLrW6IoedwMyHSyxqDnegDcslRSlYOXUedzOB//Af6hxKbymiQf72WYPj89rRWVoEHNM1+ZC9VJ3+WYNOEQBYZV5PM74L8JmkqBtUEVw7nNS7M0POYbZWcgrvYWL/95Il+rJxmZOvWXJBsxI1izfsxC2vzsPkj5Y7Mkf96FvDbPcRlGgLi3giYdkXjAOxyixklZ4iSOSVt9zmIM3qKuKaudRqUkqbikzyvikO/Xy5uROin4zTTv0AnXDvO9/4Psdp93+Usa1+5TZdUE82OeG+6bhsin39RyNyYEnKw0rWWFloaP7v5OE4dWSy7p/Z+PutA3u4bpMb3FYrOMnGb1E435vVVjXT2oo6nycoBD31eZL/3dRjBIDTtDqLToJ2ckFRCVaHH1AJwFrVLk8eKid3M977eqPndvnh3D7p9or6gQd2YDheU2BM+dg6maBXTJ3XkcwG36VM39HKbFY0bmir8JFziqx1+s/cdTikbydUDeyC6kFdlft7cSQ+SqH21w/Kab5a06Q8x98/X5l67Tbk/8nvV1t+XqY94y1xnqpL54QSRebm7z4xw1XbvHK9VIxb/k3+MWOlanfHiFB6GfluiwzTZmWwgLTpV/zG0286Qfe5W8vek58467OqhZ+Vtssq4lXFMz7vrdVze4KkwcoV+1u9CeGpyV5EBdpM+mUlsdR8MHWh2sf1iEFJrV1PRTFyr2kJZNP6EIc1Sm88ZTh+duqBGRoiM84/XG1mMxveR2ruCGZpGoyIYK8DDXOv7IYgjwWCynblaLhnAi4+IhuuL+4pKsFKDIBxi8lELm/xvwXhCEtuqBmY/E7D2+tXDTt9LggnTpyIiRMnKj+LAUqVlejXTPFxl9LgVtJWmdPdsKc5jgXrdqB+5TZligTAWrvppr6XLFj1lKKiJn+0XLl/Nynqxlg4VnDV0YNSr+VJ42Sb1adwYr2k9nP84l/qjOU/OTnTn0qlsWrwWc/r7Ic/xi9enovvPjEDF0+egRe+SPrCGYuXX3vcYFx/4jAM69khtTD6fPkW7FZUUXCDKmpW1jy9q40BqjJYgjNG9cYlRwzAb85OCmny4r1jm1LPtfGEU7zsKNxeW1QM7dEe028+AXedNwqDJC2ClYb6yqe/wFdrmtBg8jwBmUKhV5pbE5ZBPZt3NWOvz9/OLXIwRsM9E/D/pDQHghG9O+K2s0fiwF6yYKIJznJUoMOF4jYTHzWrIuSj+nV2LIjIeHnKTjyoJ244ebhjP8E35qpNw2YJQsX3cFI9AQCqBnbFyz88Cv93Unr8abhnAs4fmx6fb5KKNUeVCLkXZx+x+mzcZe6Q2aasJDVYvzJrDf5sEX4ahXws7UsZ5pyQqcGZ7TOXZW1trelnVukWAGsfrCDo6MAJ0ogqElTu62aWHqsIrUcudZ4fbG3TXhx/7zT8+8dHZ9Sm+2hxI443mAa6d7QfWGVfrQO6pidXu0FSHuTeWZBpqjbLC2Pn/GqFWRqA+Wt3YP7atDZo5oqtSHDgV/+ep9uPMYabTj8I32zYifcXbsSrs9bgZy+ZT9xOKSuJoeGeCRh0y5tSW9NPtxBUrCKq2pSW4J4LDk29l82Wxhpxgm42QvnDU5dgULd26NCmFKuloIkFd5yh2+97Rw7EuEFdcapmcrMzBX7nkU8BmP/Ggxz4wiTzOFk/Cwf+5m3b8xx8+zu+chDFExw/f2kOrj1uCEb1s9fyNBpqiarMcd06lGfkW8rQWLkY275s2KbcLvqg6jaa5Qm0w4sV2G3GdLN1ptlp7r3wMBw7bC0Oc6GFM7Me5BNFpbESPDR1CZZsVCfTcxotBjjLc6UiG7lhcglj1uYNxvSC1y+HBRsGefoo81B9M7+49gphTOQVA8yFEZV/05zbT8XTV1Xj6GHdbVqaRIRur9q6By/VrclYWU58ti7jGCd+gPJ3GupQ9Q/4CKFXHNbWYfHYpYrC0mYYhSoZ0XajUHVuwNUFBEKwLnNRwFZXMDzGlAle7QSTP7+3GNMWNWLe2u1YZ5NPTDb7ePGpnO+yokBUSv2ta9qL1+asM/VvNfKFoei5mb+QEdUv5beCi5WgEWPMk/ap0UMAp9vv4VaZ0LltGa44apCnyNnZt52KL399iuvjokBRClYAsGLzbrTGE7j/vcVo8hhSbGUeMKNTRWlWs+sGRX19Perr1QOW3UOTNAWmO+Cl/YN9zMxWWR/efAI+veUk5WdiPJCTOcpOwmbaGJWAU9muHCeN6GXZxgmH9sGofp1w0oieOGZoWgBbuH5Hhs5+f2tCF44OAKMdrMA7tEkLNaL5TsK6PVe5V4ypTp3HP3cYUWmHys8L8G4esBvvRYHm3p2dB7LIzxID8P7CTJcCN+ZBuzqD8njiRbCyS5AJ6J8rr6bNoBERkFaVCWTihnujmuxVTunM4EPn5NuP07QucnQhAIwf0hVL7zozVYNQfT1v97iNi2HWawCQmQkzGwWUu7QvT2W9zzeKSrCS81e9OW89Pl6yGQ9OXYI/vvMNKspirjM6OymtIuhX2Rbv/+x47NjX6jgfUhAYzU5Oqa6uRnW12gnaLvO6V1W2U7yYpIQp8Ks16dV5G+l5kFMS9OqU7sxecwo9etlY/Pf/jsPTVx2hm7zjCY4352Umnp29Wm8y2Gxhrha0K09rrBhjmH3bqbqEgGb4KcaqwpgQdcnGnRkCqRxwUHP8EM/XMtO2eU2yaLeSPlcLQ1elTDBDFnTMFgFbdzfjiQ+X6TQAb1x/jHLfo4Y604wC3qICjaWVltx1ZsY+z00cj5s14XXLrmbMNfjAhcErFnngVDjpy8cotNCpqEBd5nXr5+bXWlCEMXfT364epzPFqx6PGGOexs9y7bQ39DYXNJPj0rGpqEGn/Ub4k543Vp2WIfqqgtxSZIJVenCfuXxrKoS0aU8LKspKXKVX+NlLc7BAESk06fghGTb6Hh3b4NNbTsKwnkmfGFU29K/WNAUaui6i8Nw4WDvFzG9EbLIq0hzU9VVYOUiKCVlOb7Bzn9rDX17YbnRQ084OefJWCVWAXuADnKXkkP2qgOQKz8opVuBZY2XCob/7X+r1og07cer9H+GRD5YCSDqhPzZ9KRZLlQ1+dVZmJJ5TzCLbnJokjdjdCfGcu7lnsjBlJbfd8/Y3+O0b6Tp1xpqPgqO1HGV3n28fSr7bQxoDsfgSzvJmz5D4Lmc++BHOefRT19cJGqe1NoHkc2lX7uezW07CD7+VKfRn+ljZj27iHhqvKeagru3L0amiVNkXvGqsxCFWj2pSk94ZD116OO4455CM6DszfqEJ1WYl0vyaRguNonJel1fRG3bsS4Umi0zVQDLizGzClTFLnHdo/0owBjwlhUyrNCxNe5pR2S4t9Ng5lbrlrfExnDojgXd/enwg55OJWQQcM+0vm34YZqbUMgt19LjB5g6Rt509UvdejhqtW6nXJN3sweTkZMx5ymGIvYxXNblVe+RIQyPiJ+3ctgx/v2Zc6pmVadiSjDib/NEy1K3cio+XbE4dI9O1fbkrf0ZBk0lC1Y4V3hLh2jnvignOjfle3teoETM6yj9rSGnQu1NFRsHmThVljscFkR9qaI/2WNZoHv23ace+VHRqJ+23sXu2hZZmm8lvIJhiiHQ9/sAe+MhlqgcnuDF7rtm2B8/NXGW5T1+ThXUqnYa80eZxED55ZjnJyktj+Op3pys/Y/CmsXLYNADJun3fP2qQ43NefMQAnHZIb9OFulsn+EKnaDRWe5pbMwQmY8TXMzNW4mSPRYsF44d0xZGGSVzlhzLmDveZkN3Qow1D3aHb0cnjhGOFmeAkNjEG7NXGvCpvKVksMevEbRRaiy9+fTIurOpvudovNyzxrEwGkzyYsbwOOnall7xG6Vm1x4k2aWSfThmFdEUIvygdtKc5nhKqgHR+GoEot2PHcxPNy2wYmfStIa7NjHa3MCVYubjXsgwWY8CQHklN0EMOokiNQpVbhGAlC1XfPizTsV8OJhDO63aCilG2NEuX8Mi0pbr3z0rm6VH9OuG9gBZ7bvJsNbcmsE7TPst1AAXGxZUO7XuLZ8GJzLNFWzR4SWCbrMXqXbLKhozDGLO0fpBYpadoNFYvfLE6Y5sqx4hfJ7ySGNNpogB751CnmeCjQjNn2GbxlRiAVdpCrd5n2gcVsqVi/JCuOO/wfti4Y7+ygGfPjhW4z6QoZ3lpDM2tiVQ29tm3nYq7316oK749fkhXJBLAT089ED06tnGcj0VGHuhKY8yx35Yc9i/zxOVj8ddPGzw/q6qB94nLq9CrUxtHPheMpbUcgsumfI7Pbj0Z+0wm25F9O+GbDTvx228nJ7Cjh3ZPCV6vX3cMljXuQjzBcfPL+rxaR9v4F43o3RH/+uFRAIBbz3RvYrS7g6JvuolqkvdlYKkFnFl2/yB5ffa6jG2//fZILN6wE4ukSOg5a5pw9LDu4JzjlleTUZgzV2zFOWPMS5sYBfI9za3KZL3yePfctXrBuG1ZiS7xchhj3+DumRG0xw03f85SPlYmSX6DJhZjnjT+oc4iJFnpKBqNlaoD3/12ZskGP7l6AHVYvx1ealmFyYc7yrDdxFrKkHyogi+kk0Ye4M8a3QcXH3EAbpASWk79+bcAqMOp5cSXB2kD/ApN29KlfTm6tC/XCT6JRFJYPmpot4wIH6fIT5Td43XrmSOU22UzzRmj+uDFSUd5agug1ljFGHD4AV0sjxNZog8bUKnzVwSQ0gaMNakDJzQkY7SaXnIS08MGVOL8sf1xUfUA1aGW3HHOKM9mQACpH+es0ekUHrIfScpvxaMQG2PpbOhB+7apMKYUAJJmn3duPE637d53FgHQ53Yyi64T9RGNj42T5LDGlCTNBitBNkpuqdgp+Z5dIDlgD9f6tJWCKC0oOw8LdJOeI+N68Bd5GYaMQ6ZAPUUjWDl5UC+s6o9/uYw0MeLEediIk3DnfCB1i1l2fazkTqy630N7dMCjl41NaTJkfnrqgfjm92cgxtITxVlSOYcSxnRCeGsi4Ts9hqzBkB9DlYlGlY+qvCSmSxPhF9UYKBekNmN4r45464bj8PNTDzTdR5+xOo1wzhf3wqyffPP7dAJMVWFpI34L+gq/obvPTyf4lJPJimfB7SMwfkhX3HXeKDDGUsJEmGlWzDRu+mdT3WnbaUK08RxyxNu+ljjema8OzADSRXj7d9Gb4Z74cJlFq4Pj2RkNqdfy7zBayydlJfQaNVYc9hrMLu28Bw0xj3mswtRYkVilp2gEK7PSITIv+xSqZJ6+Kp2qYERv68iLXK3a3FBXV4e6uszElXYIjZWXZHVOkQdGswl6wqF90LNjhfKzirISlMRYarKX0wWUxpguKWic+58Q5TFYFvBVp1VF3Sy+60xPCfbM25N5Lqea1pF9O6XMoeOHZAYEPDR1ieXx4juLKM2+Bt+rirKSVHmh//edURnHG/3OgvptOrctQyetVJIsX4hnwe2K/IWao/C9IweCMWDzrqRWaLuN07cfnGhIPrz5hNRrEUUp3z6VKR0AyjTzsPFWy6lcrnhqJn74j1mYtUqdafyBS8YAAC45Qq+VfGWW9zF3nE2GbllQlDVz8rjwi9NH4M5zR6UEPxXpwsDSNpu2mTnCO4F59LEKM71YkONTIVA0gpWXCCQAGHTLmxj2q7dMP59xqzoh5UkjeqVWvnad7Inp9kJfrqmqqkJVVZWrYySFVVaR+7BZ+K8d8kQpBzHEYsmIHFFANh6Axkq+lqzJe19RuDsWYykTZbZQfZ0TFOU97HihRq8RXLBuu615SNyLlFO4QiC476LD8M3vz1CaXmfddirevOFY1201Q776V787HQf26oA3561P/TZrtJxz3k2B6eNma0LHV787LcP530yoOdZhdv9fG4IOVFrFgd3ap8YkESUr572SJ0c5b5dYvBjvwOtz1mHQLW+iaU9zqnSLXKxa9q86tH8llv3hLBw3XP+crd7qLLmnimMt/KIAYL2UKmXjjrRgJRc57t25ApePH2gpGAitppt0C37kjKTzuvfjwxBxSKzSUzSClROuOnoQTh2ZmVG7NcFNM0f3scjILAZjeVCWTR2CRSbldezoGnzAnyNO7GTtuZ7txYt8P1U1AN2eQ27vwvXJiUFoseKJALQiJtvNCgjLz8NIjxmSrduj8LEKwEw14aFPHO8rtF6Duqk1BUYfLkG78lIc3Du4e2KcUIWQfdPLcwEAD2n5uOxyIJmfP/1amPw7VZTh81+djMMPqEx99uKk8crjv1rT5Og6xqCKSpMIrt9phYdFPrBpi9I59c6SSkXJ/olCwDU+I/e/n0yQLEc4/+a1+anXww0LhKBNoV6T95o9W2ak81jxjG1mqJLwOv36Mca85bFyfURwkI+VHhKsJEb27WSqLbj11XT9MqeRLGIgqZdyIbnt1CpEB9+aRdesmpoa1NTUKD+btsNaopO72Ehv/t6WyAEGXju0ruyI9PrdBUlNxcwVWzFz+ZakxsrnoGEmtHxH4WNlxK8PkQpVc7zex9+fc4jLayev06+yLR773lg8culY19cMsnyG8VRy0uBgzp++gLG47L9/nM60PsJEWNzhIKceoA8GAGA6jskO6vEEx18/TedPO0VaVJ52SFrIEs+gW3NP9w7mfkZeM+XLyPnmVMjNdar5U55H+88N/61QCZHP/sBZ6pCk87qjXU2PzzkkV+kgwUqihDHTlamIHAOc+0QJPxI7M6RbrUsuViZTpkzBlClTXB0j2jVXSkg/pnPwPY7pBCtv59AVypXOIfIrTfp7PS6u/RyLN+4yrU/nFLOoQCcJPuUJ6IKx/R3nf7Jsj0lUoBcud1lQXF7InzW6j66UkBueuHws3rrhOPsdbTBq72RfHN1+HgVP+bBDFYV37zpvVMqnzA/tyvQ+cmaZ6E8/JC087WluxY69acFNXkDIpYPEK7fPiNU9e/VHR7s7mQK7YfjlurT/lqpUjWNSGquMTY4Y1rMDZv7qZFvTZercqdqE3kb6UEyBJFjpIMFKoiTG8KSDDNhCBf09LQzZDLuSC6Lj7DExCZke52rv4Lm6R9J3QaWuNvav7/QOvsfJq0Gv5gX5ODkr+3UnDsvYV85r5QWzqEAjQtCSv5LsnP/n7x6GN4MQJhS3zLvg4O44uxprTjljVB+MDEDINGuO+FpCGOpbqQ6EcHP6Xp0yz/G9Iwea5lkDnBXjli8kBCIzjVDHNmlBdu7q7bqEpPJiQzYtCq1bUL8dEEz+KjuN1Z+lWq679nvXQKZ8rLSR1628Uxpjyt/ejFSmd5fXIVNgdCDBSsKpiUHkpVGFxsvYqfG9Di5hF5cXVdSdNH/FnuAbK/9MXgUCeSCQo4S8Fq22wqyJxsftrnOTUXDyffWTD8cMszxWuSDEjANKTOtOag094aCkia1NqTcTvl1qECPGdBXXn5Qp6KsQl4nFGMpKmGn6APn7Xv7UzNRrOQ8coPZBDPK3M9a59IJY4HZ0ENFqVoLMCcY0Vsltzm+G2xQ84tRec1mFIeNErFuHDglWEk4HDhFJ43fSMzpfOi2YHLbGStwn1XrReEeO7poFwSCAEV6cwphXR6UB81rg13gtI8ai3yoNzFibpJ1BtcfPivNgFw72UQvLNmuNmAw55z4jvNxpV3sbgmFKHT7rYq9EgoMx5vg4gTHthd4HMfnfzTNiN5Z1CaA4vAg02Lm/FZtsSgGJlBdeMNYKNK+UqqeLZuZ2q1UXu7sd58NNtxDetaOIL8GKMfZTxtgCxth8xtjzjLEKxthgxthMxthSxtiLjDH/PShHmNW+MtKaEJmU9bfPOFGaIaKBjBorK2dPmV2aIiysqMCY1uWNGitVxy7Pguju15kcSA92xmzTqtXlRdX+fGDMJqQrDEVQ+3fJXMWLTOVBoowK9HFPRSJWq8ShqWtHbAA2CnrdtAk/LVj5uzduD31Iy/ckcDopizbGOQezOM5pe+T9Ut/fxXepvcJdqhYvtEqmwNXbrNN8tMS9Sx0qDZKTWyECEtwKueKZdKuxIlNgdPA87THG+gG4AUA153wUgBIAlwD4I4D7OefDAGwDcE0QDfVDk6ImoIonP870rxIFVGX++mkDgMwq7s9PVIdMXzBWPzFP0DJ9i+SD3z9K7QD85/8twl1vfp2xfYO2+MpmVKAV4qFRmQKN3assC/0tiE5sdg7VhOT3emZaGieTZjYGLKbo9X4uIyYOJ3UUozYAG1sjAlO2723BnuZWJDRBxfP5te/rdNFU2a4cp0nReaqwffV1kv+FIGh2nNn9N2pL5f2YYpsdo5z6hgHY4SzwMQPZxcppjuVyL7U+tf/pPFbOjhPmWLeljOTf0gvR6mHZoeb4Ibp0JVHDrz6hFEBbxlgpgHYA1gM4CcDL2ufPADjX5zV8s2qrfU0rgTHqqruicGqtJlAtlyIFAaBdG7XJ6NuH9dG9FxNRPC40P8n/xoikhz9YiikKYa+9dpmfDMleFxo7dizGjlWHwluZAo1kozyaw7nG0zlU5l2/woAfDZtKCPKLqjV+cgy58QmJuo+VbJ6va9gGDp8aK8UrO86Xatk5/V1kLWSMuddYGbXt8vHi+7v57dwIMGuarU3tzQmOPyxOoKlF/3zJJjm7QCHBYIsM62aoMq87+Tk/W5bMffj58sz6jVbko/N6rtdLvzrrYF26kqjhedjmnK8FcB+AVUgKVNsB1ANo4pyLNcgaAObl0nOE/IC+WKPWKgFA53Zl+O//HYv3f3Z8apuTweSLX52Mey88VCmEJc+hP4kYtITGSozl20xy57QalmNCq93HPlrfM/X19aivr1d+ZqaxUnXsbPjUBKH1MBN2ShQSl4dFrg4/wkQ2NDxq53Xv10lPBA4yUkdsPW383nIqFQ5NWPTz+8XEdZwfc5CU08qptsNoujMzP5nd/26GsUsfIJJ5DTvc+EE22zw2b2/keGkdx31LOU75LI7JDQkkOPCSlE7hixXqBM5GfnP2wfY7ZaB/vp0KPF6Dk1L+ch5Ngd4qDfojaprosPFjCuwC4BwAgwH0BdAeQGZacfPjaxhjdYyxusbGRq/NcIRc++3IIeaFXa8/cRgYYxjWM51cz+rZbq/ln+rZqQLfrR5gul+mYJW87aLj2U1Ixv4pBKsgfI28YOm8noMmBfG9zQYC1YTkd9Bw62z/9R2np16395hZ3oqgowJT/j2KB+Km0/R+V1EffzOyefNg7o2b7y0HVDh99nS7MXOBzLmPFct4na3Js9TmtOIX+e9Gjs3NwOMNHHsMz5rQDtlhVj/UCpXGKpsLBKOzvFvC6GIR79Y5x89a/BQAKzjnjZzzFgCvAjgGQKVmGgSA/gCUca6c81rOeTXnvLpHD/d1ytxg/NHNwnPtQu2NAtAPvzXUcn8xIBvHI6EBEYKVTTqWjJVLa0qwsj4uW7jRWGWDIMZ3M2FHNSH5jUJ0a2ZrV16Kb35/Bpb/4aysaPyCzGMFSIK2YoFw/UnDcem49KIjyKzpQWD82vJX4JxrPlb+TYFuzmGWqNP6OnrTnZmPlZdvktZYZR598+kHeThjEpG/qw3zX76lalAXPDptKb7ZsEO5v8CLyTt1hMtmOkkDobyeC9O6jlCjAqPVr8PGj2C1CsB4xlg7lryrJwP4GsA0ABdq+1wJ4HV/TfSPKHD6izO0QcDkGZAH/cvHJ5N/ys+qcTXrtDxNpmCl11jJHSihUB8b+5fQWNmt9PzAGDPtLOmJ1Pocfb3lVLQliE68zSQbvmpC8l3SxsPhFWUlWRNCgh4Dk8+KueZVLrwbteHXSgvDIZzBvZ8/re1x0SYPCXDl3ax9rNx/mXSC0EzkTO5u6ViRFDzsYrHXKOo0Gx+1+oZt+NO7i3DOI59anqu9iR+sFalM6NJs4OQ2njW6j/1OVtdz6JCfcby3w3wRsfVS6PjxsZqJpJP6LADztHPVAvglgJ8xxpYC6AbgqQDa6QshuIhw9p0miTvlZ+MIra7Xnub0vsZyN3YTnxA8jIO3WIUKQU02VaoKi8YNo0g8ZI1VygfAZr/B/nMAZo1mkzAitSnQ37Wi5n+QDTNGsnCs+jOVI3RUsGtNggejzfN6Duc+VnrTnamPlYdmiFOpfrsu7bxn0xELlgS3btTfVmc+WAnDL9ekFbjeb+PE7skUqP1PRwU6Uw15DbJJ57HKn3QLpLHS48stl3P+W875CM75KM75FZzz/Zzz5ZzzcZzzYZzzizjn3jOzBYSYQ+00D/LHr81OWjDnr02rlve16DutU8HGuJsQyNI+VunPDvzN29hucGI3OkGGLVi9uiU5mNY1GaN08gezsTEbpsCoDTrZWF3GWHIBoEqmqi+aHfy1/WAp6PHk5Obn5/P72zvVlsp7xZj5M6vaOryndQUJIYirTtmhotSzOVAI3HYLtEv6ZV7YmJZKLksFmAs/nkyBQtARghWcaYXEb3/JEeb+t8rjtP9ufd9Tu0esjxUjRZF5XQgm9tFd6SdytyJZ6O79rYgneKrsxBCbkjYC4yBXahCsjLb0FVt264Qp4yARto/Von3JyXOlIouF3KSwS+9YYTZfqUyBvp3XIzbQZSvSMMG5Trv667OSEVi6ySwi92LCoUkzjd2t8BkUmNb2eBxpHadb0O1mfoxK0Otmk2PLyseqTWkJrjtxGBrumeCkmTpSC0ybcaKbonnG0bmLoZh3kGNPulagtM3Bz2LmY2t7nHZgPhVhJvQUhWAlBBe7CUX+WKVSPuG+6Tj1/g9xxfhkQk+npTyMY2NJhmCl/zye4LqwbzONVTZ9rJyQ0e1Dcmb3gvgN7j5/tG67ah7zk+MJcDbQ/fGC0fY7BUQ2FGgxxsC5fjIYpmlClFm8Q+b+747Bl78+xVKjxMHBOfelsRSTslfzq/MEoenzy+VbTjm4p6E9mTgdF4P+6VKmQJv9HlyuMAUaNhkzq6scv48Y1MVV+wRpjZW7dAtpbaO7Gyd+S6MLiB1RHm+LjaIQrNIaq+QDO6CruvSMPMBsMakttbxxdzpfiFNToDHdAjMKVgaTGjcOEvrzhW0K7FKSHArLVBm8pddR7ujiFrczpDNQTaK5SLfgNBAiCLKTWywZeCE/q6oFTVQEq/LSGHp0tE4Ex7nmY+XjOn6FkhIfnXzub0/DY99Tl5aRE3ja/SbpBKHB/nZCZvRSbabVcIwxsa8QSi6q6u/bz03gNt2CuJ5rjVXKqcvdcanrejuMCJDiEKzEAK89se3Lk9Eof79mHCYeNzi1n/xAnjmqt+n5xKTsOMeM4b0YLEWtK6MgFU9wfaSg4fM1+5LvW0KSXG7pnwzTGdtZ/82MzYmyYCUwCyzQ7+PvGk4GdKeaiagSYwxxznXaVVHWRFUeJR/gmo9VEMlTvZ7DbZ05mc5ty1BuSCEjgmMqpFWRXdPEx3JTxh5QiT9deKjntgGy87r7Y+Mw9lv99xRD5pAeHTxljpcxJsB16lTuVdMthDbXPlb5MOAWCfk9mjtEpDAoSXWQ5PaSGEPb8nSuEXmAOX+seeHd9Erc2fWNg+qG7clK7O8u2Ki1T79/nHNdJzEKVq+sS75fsjt7PWny5MmYPHmy8rPOJWoTJhD9BJACMUgaBz/VYOjXFOjk8Kj5YbklFmMZqULEs6BPBZBfXzQZFej9+JTGyuPxQd8vodkRUc+AveAv2iBHLJ9+SG9cZJEU2Qlp53Xr63dTFJs3jj3G8mLyGC0W1F7vpdF5Xd5mRczjb+81KlCQXz2sMCkKwcpoChSUMKaLupHVu1YrRdGpnfpNGDt0t/ZJb8wnPlyGkbe/kyE4JRJ6YcroY3XVAcnzjavMXheqqalBTU2N8jOhdTfzjfj+gNTIEHmMP3PHisxR3K8JwcmAnmfyRgYxlpkqRPQtXSqAPBtxuO90C97MQQI/GisVle3K8cqPjsbDlx2eGg/tLiHa/uyMhoxtfhACz0tbyjFmehz7TGyCFzmICvzP3HW693KqG6t0EU5ghuHMqWZIfD/3pkDvmjyABKsokGfDnDfiBl8PsRIoiTFdpKDcAYb36ojJV1Rh4R2ZVXqEtsPpJGHsWEOl8OY9zXGFD5V+rWLUaHlziQwO8bWNg5t4279C/z6K7NBymTkZbP36sjk5PN/V+DHG0Gp4IMTEPXv1ttS2fBr0OZJ9PRgfK29n8eNjZUbVwC5oV16a+n3sriDavssk/5+Rf157pKP9hOD90Y7kYma7ulSqUsCwa4lYmDLmX7hNRQW61liJ++vuwuLcqmTRVuR6CHnse2NzfMX8oSgEKyGYiIEk5SMVYzrHYmNnOf2Q3mhbXpKh6XLtY2XYzZibJsNZPcF1WXcznNtNzhsktbW1qK2tVX4mxnrVApNBVmVHHydmPv+mwHwSJ7zBGEOLYQUgJuT1TftS2/LpXnDNJO+nzSkHZo/HB62xUp3b6fcbM6Ay9dpKWBh7QBdH5zP2KzMNeO3KzJEkbpNUVIyfSY1VpubUDWmNlfCxckaJR4EulXndrY9V6gTujvOK18zyxUBRCFbxlD9N8r14AEtjTL8KMXkiRekFQWo15PD6xg6SMaAonNdlnZUx7DYlWDm8vhcmTZqESZMmKT8r0VpgprFihvdRxpmZzt+dziNZwjMlsUyNhnjOzxvbL7Ut6oLVw5cennp977uLsHHnPl+/n18zVDbvV2nMncBxpjSRWh3S1mHhcKNjvZvxYkfcus1xycdK5evnhtR4xlVbzYk51AhmHEc+VnlPUQhWQqUqBqmlm3al3v/p3UWp/cwGi/blesFKPO5OBz2jj5Qx/F5lCpS3ZSSKC1liSflY2Wms8kCycpIKIRcaq0P6dvZ1Da8YEyt6JcYY9hlyv4nb1kEqRmucTKPGtw/rm3q9dNMuTF/U6E9jJf57ndSzOEsKc7jjtDEBX994X92MFw+sty5NI8qPMUljlXMfK4+ae68+Vvkw3hYL3spv5xlmzuu79utX2Gb9zlRj5bCfGmsMGk2Bmc7rXCdMGcva5UJjZYW4jcZcMoJ8WDEN69kBSzftyknEnpPn5IBu4RRWnPrzE7DVpCC1G2JMERWYSo6Zxq+Qmm/41Thlo66jEacZvo31CI08eMkYrDBE51lhfBTcyAV7bDRWj0xbCgD49+y1ktbQxQV0CNNcuoVOftZ0Ogl3Ek/Kx4oyr+ct0V4+BoTReV1QGmM434GZwihYiefd6Zi5p9lYvFn/ebNhpW/UWIXhY2WFuBuZiU2T//Nh7hRtdFKLzX9JG/PjbzxlOLp3sE5UmU26ti9PZUj3A2PpvGwCYRLK94W0nxRjLCBtSTbxGn1m5Jwx/XDjKQc63j/Dd9XFtY7uaOLprrFNWyzs3t8amI9VGmctTZemcXu9TEHOCfnezwqJohCsDuzVEbeeOQK9O+vVx4wxjJPzuZgcL5sygPQD73SwzBCsjHmtduzTvW+NG3ysEiaClaOrB48YD80yJouHKpsd/aBeHXVCsVtS5gEHUqBvrYPF4TeeciDqfnOKr/NHgZIYy4heLRT8Oa/r/+fy2k5xrrGSXgdwXZUp8F9rE5jVZN+e6g7pMVV1i+TFb1q49dZOlY+Vk1N5NemJduZDuoVbzxyBQ/uH48YQZYrCFDi4e3tM+tbQjO1tSmP6wcLUFKj3Q5FzpDhhT7PB5Gj4fK9B8NrbEtd14gwXq1QerXAoYZrzusnnRp+EbPDuT4/3dbwbTQJFBdpTEsuMChTku++Hn1/PayHeIK7tFKc/j9yWIB5p4zk4gLuWcAAcc05IO8Af2QWYuS1zX4HI+i8jl1NKJ+r0qrHSNEgiKtClj5XbkTCd6d3VYSlyOdpM+tZQ5dxa7BSFxsqM1gTHV2u2S1ucmQLdRgXuNfhYGVXSRh+sW1+dp+tUbotxZhsx5CnTLbD8eKhSpkAHjfVThDd5rSIQrBhLaVbvvfBQNNwzIfWZmJDManRGHX/O69E3BTodXrKtsTKjS1nmfnrBKvMYWbBKRQV6HJiUGisnPlYis7xLTW5aY0WmwHylKDRWRq47cSgenbYMg7u3T9UzA8w7SwefPlblNrO3sTI7YJ15PRcdyMo8YGYK5NBHBc7ejsjixu8iF87r+U5JLJ0g1Ph1RVTtof0rc9uooPDx+4nJ3OspslEw24hZT6/7zSkok6QRXc3HANplFKzcmL70Qg6D8VukrAoxd31dhVGD5FjD59l53dtxEVt/FzX5oFwInJtPH4GGeyagc9synf+UqfO6wcfqtTlrAdh31H9ck8xAfM4YvS9Q57Z602JzPIHDDHZquY+YCTlhTdiWGivkJpLJL27qeDlxcHdyrUImJmmsjAzo2g7PXXuk76K9YeFHY/XWvA3JFxGWrs3Gl+4d2qCzlI4j6IhOowbJTC5QPVZi0/MTx9tqrIJKtyALOk7GOKu0NJbX0/57NgVG91ErGopSYyUjDxZmz6PRx2rllj2Ozn3s8O46k4jqmkAyOrGTJGydfWgfXeh61JzX7QaMvBAkUn4TTnb167yeDzfEH6UlDPtb07mDjBw9rHuumxQYwZi9AjhJlnA6gevGygC+T4bzusl+CcUnYkvvzhVKgUn2g/WbINR4TafO/umowNz4WJHCKjoUvWAldzZTU2Cb7N6mlnhCN2j996v1+O9X61PvjQJMLpzXq6qqAAD19fUZn6Wc1xWmQCA/1KDpJKb2wxE5r9sja6wK7dsG8fu5PcNfvntYRhoWO7p3aIPNu/a7vJJznCxC3eA0QaiVxoopziNTVhKcxkpclMOZYBlzsXjTHacNoJTHKn/Jhzkwq+j8BkweyfZZEKyG9Gifep3g1p1eaAIEudBYzZo1C7NmzVJ+ZlaEGcifTp1O3me/rzG4wC35ck/8UBJjaBWCVYF94WxoZ+w4f2x/XDLuAFfH3Hy68xxSMk5Lp+hM4oH4WOnfm4mRVoKVrJGSmTC6NwDgF2eM8B+ZaYgKBBy6EAjn9Vz5WLnam8gmRS9YOVFvO6195Yafn3qQ7r2VUsQsQWhYM3bKFGjYbhwHjqjMRWu8IdT0rWbJuAAM6Z4UfsuchA5aXsvX4XmBHBVYeIJVABqrHNyTE0f09HSc06i1wDVWNqW9AKA5wbGjNXO7HEC0c1/mDr98ZR4AoEu78rTzutd0C9r/5VpWecemU+26Zr6HdtdzXdLGcDwRHkUw5Fujj3RR79OzY/CZsY8e2k33njGGBy8Zo9zXbOALzcdK+68qaSPH53SIsKG51MFqUghUxmADtxSDj1VJjKWiW/MheMENQXybXNyTMo8SvGONVZZ9rFTD3NWzE5i1PXO788g8pH5ArxZ9kcB58ofLpfPan8xr5vWY0fboEka6q9AhwUo3WKg7y0G9OgZ+3TJDMdoYy4weFBjzWIWfIDT532xFlQ8rJzFJtFosC0u1L+p3EikGH6uSGENcWwHk+9d984Zjde/9aBx/cMzg5Isc3BOv+dacJ7wMOCrQcDqV8njBTvWxqTHGha+T1/Yb/TCdOqO/VLcaADB3TZOr63nN2E7yVHQgwUp2GzDbJwshPaWGc1p1+oyOrf0Pa/6yymMFSBE5OWuRe2485UB071COMQMqTfdZsG4HAKBpj3VdMjuiHBEWFDHJxyrfGdy9ve69H21TubaAysUj4PU5c/qr6U2BQfhYOXNeV5EWrKzbwTlPF2H2OCD175IskN6v0l2C20O1HInGqHI7UglCXfanwuh9hUGU576cUOLAFJgNjH47VoJV3KAjd7Nayyar9yo2Msm0EGGBompgF9T95lRHZr4vVmz1da1CM42pKGHuJ4KoYuyLfgRjsYDKRV/1HL3q8GcrDdgUaDyHqxjIVDoFm92kwCCvJvlu7csBABcfMUC+tC3HaClGurZ3J1gxrxqr1PHejiOCI8JeMLkhFvAqzCnGQdCqM3gNu/XDxIkTbfd5exPH3SPT742tLBSBwugP55Zi0FglawUK5/X8/sIZixwf30f081z0Bc+mLoeign6s9I8x8a4bQUIIYXb3VY649toPxW8oO6G7KWnj2nldO7fb/FeFsawpDEiwknpImBOglbnJLCowm82tra31dBxDfpgCnTC6X2fMW7sdPXwGL+S7oOGEpI9VYeSxMo4DfsaF1OSag8WRdx8iZ/sFrd134rxuxo64M00gB0/9Bt7zWCVTOqTGYZcaPve1Apmby2SQ7/2vEMj3uc83OoucxRN52shegV/7uWuPTL3+ZOlm0/0yNFYRX5pExVTpld9MOBjlpTF075A0AfhPEBpEq6KNTrDK8++boU0O4FxutQ9+ruWU9i7TyJSUBPvDGp8Ti8wnGewUgpXNfomEu/JVZsjpRJLnsj/bPi3/4JJNJh74JlAR5vyn6DVWsjbBqrN01ezsQTLKUB/QDOOKJxcaK5FxXWRgdwpD/nfwa48bgmuPG4Jtu5vxxtx1OKRvJ1/nKwaNlZNEu/mC8ffyEw1X6tEc5AW3Avwj3xuLq//6pXPn9YB/Y+N93u9Cs1MqDnWgsRLX8dMPYzGW0jo6vV8fLU4ulre5DH7x6mMVdrQ4kYY0Vg7V2y98uTrwazvtABnpFsTxWexB1dXVqK6u9nQsLxBTYJf25bjy6EG+BaNi0VgJCk2O9PN9vPrZeMHrc/qVw3QAOo1YFn5jNxorsa+yTqB0rzmHb1MgkJwnEi59rL59WB9P11IVfXZ1vKejiCDJ97nPN16yCffv0hbfOaxvdhqkIGrpFswwuiAU2gTrlWK4D0Fn5Y4SfgTr0hz6WLll2+5mAEgldrVDr5UMhsp26Yi5eTuc36NWbm4KjHOeCjgZ0btjOt2Cj0bvbYnjlVlrATg36/bqVAEA6Nu5wtW10kWYo/fMEM4gU6DU2ZyuaBIJHshk2b7c2e3PSLcQYZWvbAqMYvvC4OMl5v5zYfHY98ai3GepHhljhFchEYSPlVsH5lywe3+yFExFmbPnQJ9uIZjfWz7L06vsBYmL+jL8ax2HqN7JGEOPjm3QuDNdfDqe4BjSoz0WbdiJ0pJYOirQp+p4qyaIGtttRqmWOMutUJ2qY+rymSExLDqQxsqDCaMlwQOvlWXlw2VaKzBiGBOEFu5U6w55QI4KZ43ug1MCDMgoZFOgHxOS6ONhpEyx4/dvLgQA7GtxNoMHnW4BAEpcZu3sqK1FW6Ux5tdnHazbJ57gSPC08JfOY+WrqSmc/pLiq22ShD4niHZ61XIWWPfLS4pesFq0IR2x4dQhs3Hnfrw2Z12g7bj8SPNK9maDchQnMLlNUWxfGFwwtn/YTcg6em1AYf3wvnysUmadgBoTIOeZlNDKJcYKFGZUVwJ/OSSGgzok36cEKwacfkhvHD20Gy7REnjGOQfnaatCED5WgD6fnZNTiQLvbn/7VLktN05niO6CuxgpesHq4D5SxJdFZzl/bHYHoUO08gcf3nxCxmfGjNZRMrWp/AC6lidb1t+da0HB0tZlWHs+EnRW7ihRqBorP89lUL+xWYqI3yxMYNP+9D1rGwNO6sFSgmrKx4oxtC0vwXMTx+Og3smarvE41zKu69vqt8mfLdsCwLmg1KVd0gpRc/wQV9cRz9vNL891dZyg0PpfPlL0PlbdJBOc1QM5fkg3vKo5L2YDMWAM7NY+4zOXC5eckgAghmfRzOO6Ag+NiuHoriE1KmIUwziXDcfmqOA0M7mKKDuvu619JxPU5G1mCfzvRp7SSun21/7HJY2VQL7XCc5Tz2RQGivhaC+ncLCibXkJGu6Z4NoJXQiEe5rj1jsaiOAjVrQUvWBVKjnwBl293Q0j+nRMve5X2RZrm9KF+DKrqyf/Z7O1dXV1jvaL82SdOBnGGI7vnoVG5SmqVXmb0sJSFpdkwbE5Krg1ycikE4QG1ZrguOqYQZi5Yiv+cP4o18cGlavMKujhky1S2gTtv8hf1aoYA2NSagt9KRvx33s7R/bphL6VaRW8m1O57Q9Ofd6MRMmSUeyQYBWRMHFRQR3InHSNOXBy0YGcJgZtTQDlWnMjOHdEglLFsny6wuSbzxRyuoVWHzmoxKSeizxWbikrieHJK73lqgtKdt5toZU5oB3wteYC+4lWB108ZnGkTYGCUp1glb7ffoswA8nnW/yE2RaSnRSGt6LQ+l8+UljLZg+UlsgrbfP9cqnNuqBK7+xsHJOjkCfqpqHC10G/nTp1JqWKUiB9Ons3w0QRv2V/okyrMd+JC0py7GP11g3H4ZNfnpiTawVBo0XEXL+KzGcqJVipNFayEMvTZsbSAEyBMWYQjrP4uB/QrZ39TgqiJ7oXL6SxkrQJVuptOSIEAL6dxQShw3p20L0Pw/G1pqYGgHkx5lLDAAeAerYJTiOf8pmgC/RGCacJNFVs3LEPALBm216bPYNhpM/yS1Glh+YKK9YoYkEnC0tiASM0VikfqxL/psBYjKXGYRrmCDuKXmPlNP9O38q2uPfCQ1PvDzQIP0FilKMyogJz4GM1ZcoUTJkyxfTzEpVgleU25SulASbijCqxAo4K/Hr9Ds/HBp2WJSrkwo9OXlCO0uRFo2ClSvDcKvJYQRzjP4+VlyLMYRHdlhUPhT/iu8Cu48mrcr9ZfAW/P3cU7jxX7zwqO0kCwJ/fW6x7HwUnxefWJlvx9zW0frNDZQosNIIu0Bs2d5xzSCDnKdSfPhdfa1dr5jYRgRxXtEBYHxI8GcdpTAzqZ4Eja6yiqrKKYoBEsUKClYTdhJANP5Irxg/E5eMH6raN6tvZ8pgo+Fit2JP8/8zqzMgdQk9RmALlkaQAvu73jxqE0f2s+6ETVIELhUAuxp6ZTZnbYhYaK/EMtsaTpkBj/io//TDG9CVmcjX2ehGWCk1jnI8UZq/3iN0DKWupsunMbqcNi4LGygzq1JkUsmO3oETnq1gYWJWZcsqBvbPnMhAmYWklhQawhYv36XaUyBorycdKmC3LfGisSmIslYvMT14ztzS2Or/PtLCNDiRYSdg9wjpTYJizR0R7UESbFTplBaq1kJHnrELJY/WTU4b7Psf5RVDOKAiuGuDsmRH1ovcm9Mk/k6+T/1sTHImElHGdIWNft8QyfKxyw91rnEcPR3nBXWwU/ojvArsJQZ48wkwmKgizBSdqCUCNRTHCvyvRIyh/vChTiJnX25f7D5r+ePHmAFoSPYIa/q45djBO6tySKq5sh1YtC82a4MQUGqt4IgEOSWOlPZF+TIElMZZK1JxLX6aPd7rPaVUo/S+fKfp0CzJ2/W5d077U6zDlqlz067Fjx1p+Pr4Lw7TNHHJ6P9JYFS+FaO4M4jt1qCisIZaxpGAR1K9929kjsXnHHPx9lzOzq8id3MxZRtZ28T6eSOb+E0KXsWagF5Zs3KWrhhGBdXUGNP5GB18aK8ZYJWPsZcbYN4yxhYyxoxhjXRlj7zHGlmj/uwTV2Gxjp7F6Z8GG1OswNVa5cF6vr69HfX296eeHd1ZfPILjDZEDCrEIc4uPxKCCk0f0DKAl0cEYaRcUpQ7PJ/bbl8gUfMX71kQCnPPUWCTa7EfTpCsx5v002SWyDSs+/JoCHwTwDud8BIDDACwEcAuAqZzz4QCmau8LgkUbdqZe53rykAeFVm3FGKYdt1DDyAlv6PJYFYh4LZJ7+qHQNHlpP6Ngv9fRXZ2dTwhW2+Mx7G/VC75iTG7cuR9cyrwutgdZVSjbz/j3jjzA87GMJKzQ8Tw3M8Y6AzgewFMAwDlv5pw3ATgHwDPabs8AONdfE6ODnBE91xoreQhpSQBlzF7Dlk1UK0zKo1K8FGLm9TalRg9C9xTKvcg2Q9tb36ivtif/W2m2ZizbAgC49dV5uszrYpz0E83Xv0vaiZznYKAr91CknYbf6OBH6TEYQCOAvzLGZjPGnmSMtQfQi3O+XttnA4BefhsZFY4Z1j31OtcLUbkmX5xnX2PEGLMU3Jyq7ok0QYTvRxW9xqowGNnHf3mYKAS5BIl4hnP9tba0JP9byRtCO7inOa7LvB6Exqqb9r2FUJXt79+2zLtQX1hPXH7iR7AqBTAWwOOc88MB7IbB7MeTT6HycWaM1TDG6hhjdY2NjT6akTsuPmJA6nWutUUtORas7OhWuDJCVphx60mYdtMJYTcja8xauS39pkBG9k5t/TueF5pglXIEz8G1yhUXKbO4cBtJ6uLIdF73o2mauyapMtu+tyUnmqGLqgfY72SANFbRwc/IsQbAGs75TO39y0gKVhsZY3045+sZY30AbFIdzDmvBVALANXV1aE+E1/8+mRs2G7vT9G3c7rUTK4HzFX7S1CpvY7z8DVGFSaSXYHNI4HRp7PzfDT5yAtfrk69LhQfKzExH+xDc1Vo/YEZzGtBMrAtsFKqVa2qfW017smC1UeL04t1MVYba656Yfnm3YFGRZoxuHt718dEoSIHkcSzxopzvgHAasbYQdqmkwF8DeANAFdq264E8LqvFuaAnh0rcGj/Stv95MEk16bAVfvTP1UUNFaEN1677hj88owRYTcjcLxMBPnAKz86Cs9de2TYzYgM2dRYyULVuEroUrkIrMa9sw/rCwA45WB9JGaLJqGVBFAMPSW8RVh6iW7Lige/uu7/A/BPxlg5gOUArkZSWHuJMXYNgJUAvuvzGpEk1xqrAW3S7utRFaxIFW3PmAGVGDOgMuxmBM6abXtSryM857imamBXX8cnCiyiI1vpFmRO7cEwrD3wRVPmvbPSlAm/pIYte3TbD+yVDDoa2sO/8N+iUqNlmb1xjrYOBvwCe9TyGl+CFed8DoBqxUcn+zlvXpDlyaNLuzJs29OCru3LsXV3s855fXc8mXk4ihTQnEq4YHS/zpi1qgkAPQMyQYb4R4FsLij7tAHW7weO6wZs2q//7BiFfCsEJkGpJnws3bRLt/3KowbhiEFdMSqAotrnPvopgNw+41ubgX6F7UlQcFBJG49kW2M1+YqkvFo9MJlf9S/r0v5d/2vkaGrN6uU9UWBzCOECOeKxUGoFBkGBpbHKqL8XJC8dEcMtwxi+3YtlaOQfPTQzSq5Hxza696UmNTljMRaIUBUWrTSw5h2FVW8hh2R7wBw3uCvm/vY0fP+pZGzAgr25/akmT57s6bgCm0cIhzRLJhKSq9KIoIVC8UEz1t8Lko6lDJf0T563hJlLE73bABv2ZwpSuRRic/mMNzYDA9vZ70dFmKMDCVYeyYWPVee2Zajwkc/EDzU1Na6PIRt/8fKjbw1NRWLRwK5n7u2noaK8MIwDKeElyz+y1d0SbTAWVc6mpvSx743Fj/85K2vnt+KuxQn8e5zzeYD6X/gURm8PgVytWC6s6p+bC/lAdtClTl2ctC1PD/yksdLTuV1ZIFnco0DjzqTzU7Z/4k3N5p9tF8lCbRy6jz+wR2DtOWZod937XKYUWbHHfh+AXDGiBAlWHslVVOAZo3rr3ueinAIA1NbWora21nKfMVp6nxACZYiIodcekGRVqOxuViVBCJ69FpfZrX322dItlue4+phBgbWnLOzEgQ4QUwMtbMKHBCuP5Orh7dBGb63dkSOn9UmTJmHSpEmW+xzTLXkThHMlyVfFS+e2ZWE3gcgh2Q5QGC3lZB1q4l+0c7/1YHjiQT0tP3eD0Z8rF+N/mcecOiRXhQ8JVh7ZtS83Eo5xAPv5gujkWXhiRVKUWrhT2ki9uigZ0DU9+9GKufDJ9k8syxSXD3B/tfblwZpevQo5fjAuqu2ghW10IMHKIztzJFgZqWsK5bJKhLZ+437q0kSaQquPR2SS7Z9Ytry9uFY/vhzZxf74WMAhgsYFbi6e8O9omeSJ/IMEK49wWh+kkJOVFkqdOMI7W3btt9+JyEtEdvNs9/MSSZDZZVjDiknr+0cNzGobwubY4e6c72lGig4kWHlk7ba99jsVCf9YQ12aSLPLxveFyF/2tiT11Ft2Z1d4li1vNw1TT1O9OlUotwNAPMsp73OhlD3xIG9RjbS0DR8SrDzyzIyVoV4/CkEq7TU3BqGxIvGKAMwzYBP5z7jB/monOkUWrNob3KXEOGPMYwUAPbVs7HtyFL2YTUo8mjMjMDUUPTQC5hFyvqirPDh0Bs3RXUUW5jTht4oIG7v8QkT+0rdzUkuU7eLS8sKx1GSWUgkem3bmxgydi7JNbq9BCZqjAwlWHhnRu2POrynXjPrBAdnt2Jxz25xZYiUp8spQxyaAcCKoiNwgAhPiWQ5Olh8hM+28SrAqLyneKY1K2kSH4n0KfRKGYCVXfG8XAVvgyT2SbTi2a7ot4beKCJt9LdFJCUIEi/Cfa27N7m+8Xzp9mWFQEW9VpsAu7Qszn9pmF5HXFJQbPiRYueSaYwcDAEb3r8zZNbu2LwcAnPtFtCasgzsk/w/SUhiRwooAgGE9O4TdBCJLfKjVg5y/bntWryNrv81NgZkfdGlXnqUWhct7jfajK42/0YEEK5fccPJwXHbkAbhs3AE5u6ZQv7fmsOdUVVWhqqrKch+xMrp/Oc/YRhQvw0mwKlguOzI57g3u1j6r15FNgUaNlZXzemW73Giscj3ObbGonUhED3epXQl0bluGP5w3OqfXDMNnZdYs+0ruWY5oJvKM//30eCxv3J0Tx14iHER+qWzn8bMa8vZpPp37WjMj/3Klscp1vr4PNnNcP8R6HxqOowNprPKAlohWOTa2KpqtJHLFgb06ZhQNJwoLkdE824EqsgtpuWGWmrMj+f/x6csyjqvMomD1oxOGZu3cdizf42AnUYQ5qy0hnECCVR4wuLtJFdKQaRdsOS6CICJON83fs1OWi26XSTOT2bpy/fZ9Gdu6ZNEUOLh72vwZZaVshJtWNJApMA/ooSW9E7x5ZDTk4Q6GyETSWBFEYXPNsYPRpV05Lqzqn9XrjOoIHNUlmdKln0mC9eOGd8/Ylk1TYNTTiND4Gx1IsMoDNu/Uey72axvdDh7dlhEE4ZfSkhi+e8SA7F8nxvD4YdYqcaE9k8mm87pcUSBX41wMHAmHV0vnsSIRK2yiofogLDlqaLewm2DLgh2clkwEQeSMmMIel02NVbvy3Ps+/HaA+5q0tLgNHxKs8oDTDumV82tOnDgREydOdLz/A8uTObaoUxMEkQuqBnXJ2JZNjdWIPp1Sr3PlY3VS5xbH+9K6NjqQKTAPOKRv55xfs7a21tX+XzYB/c2LzRMEQQSKKpdWu/LsTWmiTmIu8ZTShla3oUMaqzzjsn7R7jVRjpYhCKJwUNUKbN8me+Y6OT9brvJYtXMxQ1Ot1uhAglWe8Z3euenQ9fX1qK+vz8m1CIIgnHJR3+QYOKpfpiY/mxqrMPCyUKW1bfgU1lNYBBiT5WWL6upqAAB3sQyiBRNBENnml8MYJlZuR/s2mdNXrhzMw9DM741ztHWQ8oEEq/AhjVWecGu/vTimKzCkfXS7DQd1aoIgsktpjKFrqXoZV1GWG8Fq1VYnqdCDZf4O689pYRsdSLDKEy7o1oxHD41eqvOHRqUfoXWZiZAJgiByhsrvKhts2ZX7qshLdluLTuk8VkTYkGBF+OK46KfYIgiCCJRsF6FWsS+z5rQSEqzChwQrwheMwgAJgigyPlq8OWfXGtA2+f+hFTYaK7IFRgYSrIhAITGLIIhCZ9f+1pxda9N+lwfQIBw6FBVIEARBFAz3X3wYhvboEHYzAmN/wtl+pLCKDiRYEUrq6uoc7/vAqBhunK+VtKHVEkEQIXLe4f3DbkKgHNcV+Hir/X7C74uG4PAhwYpQUlVV5XjfAwtncUgQBBEprj4gho+3OlRbgQSrKEA+VoRv+lbIpR4IgiAKkyMUhZ+zzbDMkohExCHBilBSU1ODmpqasJtBEAQRGXKVgFTGqXuFiAqkxW34kGBFKJkyZQqmTJnieP9juib/U6cmCKJQOeXgXjm/ZgeXshyNweFDghVBEARBOKBqYNIUeHCfTjm7ptNcgRQVGB1IsCICgRn+EwRBFBq9OlUAAM4Z0zeU6++Nk/iUD1BUIBEIJFARBFHo9OjYBl/fcTrahuBrBQAr9wAjOqo/I5ErOpDGiggEoa1u2EvdmyCIwqVdeWlopbwW7jIfX1NFmGmVGzokWBGBkNB69ezt4baDIAiiUEk4WLeSXBU+ZAoklIwdO9bV/jHqzQRBEFmlS5nFQEvGgshAghWhpL6+3tX+JFcRBEFkl/uXJ3BSD7V/V8oUmLvmECaQKZAgCIIgIsxRWsL31Xvt9yXBKnxIsCICQXTm6wZRtyYIggiSHm3sx1WyBEYH34IVY6yEMTabMfZf7f1gxthMxthSxtiLjLFy/80kcg1jLLTIF4IgCCLNWT3djMUkYoVNEBqrnwBYKL3/I4D7OefDAGwDcE0A1yAiDqMMoQRBEFlhQFv7fUicig6+BCvGWH8AEwA8qb1nAE4C8LK2yzMAzvVzDSI/IHmKIAgiO5Q7mKlTRZhpMA4dvxqrBwD8AkBCe98NQBPnvFV7vwZAP9WBjLEaxlgdY6yusbHRZzOIsKG+TBAEkR2c+FgJaCwOH8+CFWPsbACbOOfu4vI1OOe1nPNqznl1jx49vDaDiBjUqQmCIHIPmQKjg588VscA+A5j7CwAFQA6AXgQQCVjrFTTWvUHsNZ/M4moQwIVQRBE+NBYHD6eNVac81s55/0554MAXALgA8759wBMA3ChttuVAF733Uoi+lBvJgiCIIisZF7/JYAXGGN3ApgN4KksXIPIMpMnT3a1PwMDwEm+IgiCyCIb93H0qsgcaSnzenQIRLDinE8HMF17vRzAuCDOS4RHTU2Nq/2pMxMEQWSfnXGgV9iNICyhzOtEoJCARRAEkT2mNqrd1Dl5r0cGEqwIJbW1taitrXW8PwlUBEEQ2efxBhPBSvtPY3H4ZMPHiigAJk2aBMC5SZCS0hEEQYQPjcXhQxorIlCoUxMEQQRP/wrrz8kSGB1IsCICgeQpgiCI7HFqD2ejLI3F4UOCFREI1JkJgiCyh6oQ87ubEjjp0zhaEqSvihIkWBEEQRBExDm1Z+by9Zdfc2xtAVbtlYow57hdRCYkWBGBQL5VBEEQ2aNjqfkg+/p60lhFCRKsiEAguYogCCIcnl3DyXk9QlC6BUIJ95htjgQsgiCI7LO1WT9GUx6r6EAaKyIQqDMTBEHkhtYExwtr1YtfcssIHxKsiECgzkwQBJEbVu0F/rFGrbEiwocEK0JJVVUVqqqqXB9H8hVBEER2aRMD9sT129btTf6nMTh8yMeKUDJr1ixX+1NnJgiCCI9n15DOKiqQxooIFDIJEgRBZJe3N5kLUTQEhw8JVkQgUGcmCILILm21GfuRFaSdijIkWBGBQIIVQRBEdtmbCLsFhBNIsCKCgen+EQRBECFAY3D4kGBFBAJ1ZoIgiOzSszxz2w8H6UdfRokXQoeiAgklEydOdLU/CVYEQRDZ5aHRMVxSr7cHntGT4YmGtDBFAUThQ4IVoaS2tjbsJhAEQRASB3XQvx/bGRjUTi9JlZNgFTpkCiQCgfoyQRBEdmEGddQTh2VO4WU0GIcOCVaEkvr6etTX1zven9TPBEEQuaU8ljnwKjYROYZMgYSS6upqAADn7hwhqU8TBEFkn87S7D2hF8ObG8lpPSqQYEUEAglUBEEQuWN7a/r17w5iGNgWuKw/A3aE1yYiCQlWRKCQSZAgCCK3lMUYarS0C602+xLZh3ysiEAgeYogCIIgSLAiAoI0VQRBELmjS1nYLSDMIMGKCASSqwiCILLP6T2So+22lpAbQphCghURKC6DCAmCIAgXHNAu7BYQdpDzOqGkrq7O1f6ksSIIgsg+dU20eo06JFgRSqqqqjwdR12eIAgie5zRk2H2dhppowyZAolAIOd1giCI7NOdigFGHhKsCCU1NTWoqalxvD91dYIgiOzTs03y/+X9adSNKmQKJJRMmTIFAFBbW+tof9HFSUFNEASRPUZ3Ynji0BjGVobdEsIMEqyIQBCCVYIkK4IgiKwyvitpq6IMmQKJYKB+ThAEQRAkWBHBQgorgiAIopghwYoIBFJYEQRBEAQJVgRBEARBEIFBzuuEkrFjx7raX0joVNKGIAiCKGZIsCKU1NfXu9pfJAgluYogCIIoZsgUSAQKCVYEQRBEMUOCFREoJFgRBEEQxQwJVoQSxhiYiwKAFBVIEARBECRYEUFDKiuCIAiiiCHBigiEGDmvEwRBEIR3wYoxNoAxNo0x9jVjbAFj7Cfa9q6MsfcYY0u0/12Cay4RVagIM0EQBEH401i1Avg553wkgPEArmOMjQRwC4CpnPPhAKZq74kigQQrgiAIopjxLFhxztdzzmdpr3cCWAigH4BzADyj7fYMgHN9tpEgCIIgCCIvCMTHijE2CMDhAGYC6MU5X699tAFAL5NjahhjdYyxusbGxiCaQUQAyrxOEARBFDO+M68zxjoAeAXAjZzzHXKIPuecM8aUUy3nvBZALQBUV1fTdBwxJk+e7Gp/8rEiCIIgCJ+CFWOsDEmh6p+c81e1zRsZY3045+sZY30AbPLbSCL31NTUuNrfRcorgiAIgihY/EQFMgBPAVjIOf+L9NEbAK7UXl8J4HXvzSPyBdJYEQRBEIQ/jdUxAK4AMI8xNkfb9isA9wB4iTF2DYCVAL7rq4VEKNTW1gJwrrlKCVYkWREEQRBFjGfBinP+CcwrmZzs9bxENJg0aRIAD4JVltpDEARBEPkAZV4ngoEyrxMEQRAECVZEMJDvOkEQBEGQYEUEBPlYEQRBEAQJVkRAkI8VQRAEQZBgRQQEIx8rgiAIgiDBiggWEqwIgiCIYsZ3SRuiMOEunaVSzuskWREEQRBFDGmsiECgqECCIAiCIMGKCAjhY5UItxkEQRAEESokWBFKqqqqUFVV5Xh/igokCIIgCPKxIkyYNWtW2E0gCIIgiLyDNFZEoFCCUIIgCKKYIcGKCAQyBRIEQRAECVZEQDAKCyQIgiAIEqwIgiAIgiCCggQrIhDEg0SmQIIgCKKYoahAQsnEiRNd7Z/ysSLJiiAIgihiSLAilNTW1no6juQqgiAIopghUyARDOS8ThAEQRAkWBFq6uvrUV9f7/o40lgRBEEQxQyZAgkl1dXVAADu0GmKFFYEQRAEQRorIiBIsCIIgiAIEqyIgKCoQIIgCIIgwYoICJF5PRFuMwiCIAgiVEiwIgiCIAiCCAgSrIhAIUsgQRAEUcyQYEUEQsp5nSQrgiAIooihdAuEkrq6Olf7U1QgQRAEQZBgRZhQVVXl6ThSWBEEQRDFDJkCiUCIaSorEqwIgiCIYoYEK0JJTU0NampqXB9HghVBEARRzJBgRSiZMmUKpkyZ4vo4ShBKEARBFDMkWBGBQM7rBEEQBEGCFREwpLAiCIIgihkSrIhAYKSyIgiCIAgSrIhgILmKIAiCIEiwIgJCCFZkCiQIgiCKGUoQSigZO3asq/2FYJUgyYogCIIoYkiwIpTU19e7O4BsgQRBEARBpkAiWEhhRRAEQRQzJFgRgUAKK4IgCIIgwYowgTEG5iKHAglWBEEQBEGCFREwVNKGIAiCKGZIsCICIaaprChRKEEQBFHMUFQgEQgndWe4qC/ww0EkWREEQRDFCwlWRCCUxRh+fSAJVQRBEERxkxVTIGPsDMbYIsbYUsbYLdm4BkEQBEEQRNQIXLBijJUAeBTAmQBGAriUMTYy6OsQBEEQBEFEjWyYAscBWMo5Xw4AjLEXAJwD4OssXIvIEpMnTw67CQRBEASRd2RDsOoHYLX0fg2AI7NwHSKL1NTUhN0EgiAIgsg7Qku3wBirYYzVMcbqGhsbw2oGQRAEQRBEYGRDsFoLYID0vr+2TQfnvJZzXs05r+7Ro0cWmkH4oba2FrW1tWE3gyAIgiDyimwIVl8CGM4YG8wYKwdwCYA3snAdIotMmjQJkyZNCrsZBEEQBJFXBO5jxTlvZYxdD+BdACUAnuacLwj6OgRBEARBEFEjKwlCOedvAXgrG+cmCIIgCIKIKlQrkCAIgiAIIiBIsCIIgiAIgggIEqwIgiAIgiACggQrgiAIgiCIgGCc87DbAMZYI4CVLg/rDmBzFppD6KH7nJ/Q75Yb6D7nJ/S75YZCvs8DOefKJJyREKy8wBir45xXh92OQofuc35Cv1tuoPucn9DvlhuK9T6TKZAgCIIgCCIgSLAiCIIgCIIIiHwWrKiQXW6g+5yf0O+WG+g+5yf0u+WGorzPeetjRRAEQRAEETXyWWNFEARBEAQRKQITrBhjAxhj0xhjXzPGFjDGfqJt78oYe48xtkT730XbPoIxNoMxtp8xdpN0ngrG2BeMsbnaef6fxTXfYYw1Mcb+a9j+T8bYIsbYfMbY04yxMpPjBzPGZjLGljLGXmSMlWvbj2eMzWKMtTLGLgzi/gRFnt7n67V7zBlj3aXtJzDGtjPG5mh/t/u9P1ElqN9NOl8JY2y28Tcx7HOldt4ljLErpe13McZWM8Z22bS5ijE2T/vtHmKMMW37Rdp3SDDGIhXxk6f3WbkfY+wqxlij1D+udXs/8oWo/G6MsXaMsTcZY99o7bjH4njqH7m5z/nXPzjngfwB6ANgrPa6I4DFAEYCuBfALdr2WwD8UXvdE8ARAO4CcJN0Hgagg/a6DMBMAONNrnkygG8D+K9h+1naeRiA5wH8yOT4lwBcor1+QuwHYBCAQwE8C+DCoO5REd/nw7V72gCgu7T9BOM5C/UvqN9NOt/PADxndv8AdAWwXPvfRXvdRftsvNaeXTZt/kLblwF4G8CZ2vaDARwEYDqA6rDvbQHcZ+V+AK4C8EjY97SYfjcA7QCcqO1TDuBj8dwrzkH9Izf3Oe/6R2AaK875es75LO31TgALAfQDcA6AZ7TdngFwrrbPJs75lwBaDOfhnHMhmZZpf0pHMM75VAA7Fdvf0s7DkXz4+xv30VYXJwF4WdG2Bs75VwASTr57Lsm3+6ztN5tz3uDiaxYcQf1uAMAY6w9gAoAnLS55OoD3OOdbOefbALwH4Azt3J9zztdbtZcx1gdAJ21fjuQiQ7RtIed8kZPvnWvy7T672a+Qicrvxjnfwzmfpl2jGcAsqOcP6h85uM/a53nXP7LiY8UYG4SklmImgF7STdkAoJeD40sYY3MAbELyR5npsR1lAK4A8I7i424Amjjnrdr7NUg+YHlDntxnO45iSXPk24yxQ7xcP9/w+7sBeADAL2At+PcDsFp67/b57qcd4/X40MmT+2zHBYyxrxhjLzPGBgR43sgSld+NMVaJpKZ+qsnx1D+yf5/tiGT/CFywYox1APAKgBs55zvkzzTJ3jYMkXMe55yPQVKCHccYG+WxOY8B+Ihz/rHH4yNLgdznWUiWBTgMwMMAXvN4/bzB7+/GGDsbwCbOeX32Wpn/FMh9/g+AQZzzQ5Fc6T9js3/eE5XfjTFWiqR7w0Oc8+V+zhVFCuQ+R7Z/BCpYaZqLVwD8k3P+qrZ5o6Y2FerTTU7PxzlvAjANwBmMsSMlJ7XvOGjLbwH0QNIGLLa9qx3/JIAtACq1HxZIChdrnbYtTPLsPltdd4cwR3LO3wJQxiTn9kIjoN/tGADfYYw1AHgBwEmMsX8ofre1AOQVnOXzLbSX2t8d2r6yar7Y+keu7rMpnPMtnPP92tsnAVTZtDmvidjvVgtgCef8Ae3a1D/05Oo+mxLp/sGDc4pjSNqZHzBs/xP0TnH3Gj7/HfRO1T0AVGqv2yLp1Ha2xXVPQKZT9bUAPgPQ1qbN/4Leef3Hhs//hug5r+fdfZb2b4Deeb030rnUxgFYJd4X2l9Qv5vdbyJ91hXACiQdRbtor7sa9nHrvH6W4fPpiJ5zbt7dZ7P9APSRXp8H4POw728x/G4A7kRS8IjZtJn6Rw7us3SuvOkfQf5gxyKpPvwKwBzt7ywkfZmmAlgC4H3ppvZG0t66A0CT9roTktF4s7XzzAdwu8U1PwbQCGCvdvzp2vZWAMukdijPAWCI1jmWIilktdG2H6GdbzeSmq0FYf9QeX6fb9COawWwDsCT2vbrASwAMBfA5wCODvv+Rv13M5zzBFhEVQL4gfZsLwVwtbT9Xu18Ce3/70yOr9aejWUAHkFaCD5PO24/gI0A3g37/ub5fVbuB+BuqX9MAzAi7Ptb6L8bkhoVjqRTt2jHtdQ/Qr3Pedc/KPM6QRAEQRBEQFDmdYIgCIIgiIAgwYogCIIgCCIgSLAiCIIgCIIICBKsCIIgCIIgAoIEK4IgCIIgiIAgwYogCIIgCCIgSLAiCIIgCIIICBKsCIIgCIIgAuL/A737G8fd7KV1AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from merlion.plot import plot_anoms\n", + "from merlion.utils import TimeSeries\n", + "from ts_datasets.anomaly import NAB\n", + "\n", + "# This is a time series with anomalies in both the train and test split.\n", + "# time_series and metadata are both time-indexed pandas DataFrames.\n", + "time_series, metadata = NAB(subset=\"realKnownCause\")[3]\n", + "\n", + "# Visualize the full time series\n", + "fig = plt.figure(figsize=(10, 6))\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(time_series)\n", + "\n", + "# Label the train/test split with a dashed line & plot anomalies\n", + "ax.axvline(metadata[metadata.trainval].index[-1], ls=\"--\", lw=2, c=\"k\")\n", + "plot_anoms(ax, TimeSeries.from_pd(metadata.anomaly))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we'll split the data into train & test splits, and run our anomaly detection model on it." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Get training split\n", + "train = time_series[metadata.trainval]\n", + "train_data = TimeSeries.from_pd(train)\n", + "train_labels = TimeSeries.from_pd(metadata[metadata.trainval].anomaly)\n", + "\n", + "# Get testing split\n", + "test = time_series[~metadata.trainval]\n", + "test_data = TimeSeries.from_pd(test)\n", + "test_labels = TimeSeries.from_pd(metadata[~metadata.trainval].anomaly)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " anom_score\n", + "timestamp \n", + "2013-12-02 21:15:00 -9.065700\n", + "2013-12-02 21:20:00 -8.097140\n", + "2013-12-02 21:25:00 -6.908860\n", + "2013-12-02 21:30:00 -4.892315\n", + "2013-12-02 21:35:00 -3.703186\n", + "... ...\n", + "2013-12-14 16:25:00 15.041999\n", + "2013-12-14 16:30:00 14.494303\n", + "2013-12-14 16:35:00 16.234568\n", + "2013-12-14 16:40:00 15.160902\n", + "2013-12-14 16:45:00 15.055357\n", + "\n", + "[3403 rows x 1 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initialize a model & train it. The dataframe returned & printed\n", + "# below is the model's anomaly scores on the training data.\n", + "model = StatThreshold(StatThresholdConfig())\n", + "model.train(train_data=train_data, anomaly_labels=train_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " no post rule with post rule\n", + "2013-12-14 16:50:00 14.520900 0.0\n", + "2013-12-14 16:55:00 15.065935 0.0\n", + "2013-12-14 17:00:00 15.825172 0.0\n", + "2013-12-14 17:05:00 13.846644 0.0\n", + "2013-12-14 17:10:00 15.050966 0.0\n", + "... ... ...\n", + "2014-02-19 15:05:00 15.152393 0.0\n", + "2014-02-19 15:10:00 14.771146 0.0\n", + "2014-02-19 15:15:00 14.102446 0.0\n", + "2014-02-19 15:20:00 15.023830 0.0\n", + "2014-02-19 15:25:00 13.870839 0.0\n", + "\n", + "[19280 rows x 2 columns]\n" + ] + } + ], + "source": [ + "# Let's run the our model on the test data, both with and without\n", + "# applying the post-rule. Notice that applying the post-rule filters out\n", + "# a lot of entries!\n", + "import pandas as pd\n", + "anom_scores = model.get_anomaly_score(test_data).to_pd()\n", + "anom_labels = model.get_anomaly_label(test_data).to_pd()\n", + "print(pd.DataFrame({\"no post rule\": anom_scores.iloc[:, 0],\n", + " \"with post rule\": anom_labels.iloc[:, 0]}))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " anom_score\n", + "time \n", + "2013-12-16 06:40:00 -3.024196\n", + "2013-12-16 06:45:00 -3.012073\n", + "2013-12-16 07:00:00 -3.468464\n", + "2013-12-16 07:05:00 -3.124039\n", + "2013-12-16 07:10:00 -3.491421\n", + "... ...\n", + "2014-02-09 11:35:00 -4.819248\n", + "2014-02-09 11:40:00 -4.823173\n", + "2014-02-09 11:45:00 -4.822201\n", + "2014-02-09 11:50:00 -4.677379\n", + "2014-02-09 11:55:00 -4.300280\n", + "\n", + "[721 rows x 1 columns]\n" + ] + } + ], + "source": [ + "# Additionally, notice that the nonzero post-processed anomaly scores,\n", + "# are interpretable as z-scores. This is due to the automatic calibration.\n", + "print(anom_labels[anom_labels.iloc[:, 0] != 0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization\n", + "\n", + "Qualitatively, we can plot the anomaly score sequences to see the difference." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no post rule\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"no post rule\")\n", + "fig, ax = model.plot_anomaly(test_data, filter_scores=False)\n", + "plot_anoms(ax, test_labels)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "with post rule\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"with post rule\")\n", + "fig, ax = model.plot_anomaly(test_data, filter_scores=True)\n", + "plot_anoms(ax, test_labels)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Customizing the Post-Rule\n", + "\n", + "The example above uses a simple threshold as the post-processing rule. As a result, the model is continuously firing alerts when the differenced time series value is far from 0. We support an alternative option which combines successive alerts into a single alert. We demonstrate this below." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from merlion.post_process.threshold import AggregateAlarms\n", + "\n", + "# We use a custom post-rule (AggegateAlarms instead of the default Threshold),\n", + "# where we suppress all alarms for a day after the most recent one is fired.\n", + "threshold = AggregateAlarms(alm_threshold=3.0, alm_suppress_minutes=24*60)\n", + "model2 = StatThreshold(StatThresholdConfig(threshold=threshold))\n", + "\n", + "# Train the model as before\n", + "model2.train(train_data, train_labels)\n", + "\n", + "# Visualize the model's anomaly scores with the new post-rule\n", + "fig, ax = model2.plot_anomaly(test_data, filter_scores=True)\n", + "plot_anoms(ax, test_labels)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quantitative Evaluation\n", + "\n", + "Finally, you may quantitatively evaluate the performance of your model as well. Here, we compute precision, recall, and F1 score for `model2` above." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model Evaluation\n", + "Precision: 0.5000\n", + "Recall: 1.0000\n", + "F1 Score: 0.6667\n" + ] + } + ], + "source": [ + "anom_labels2 = model2.get_anomaly_label(test_data)\n", + "\n", + "print(\"Model Evaluation\")\n", + "print(f\"Precision: {TSADMetric.Precision.value(ground_truth=test_labels, predict=anom_labels2):.4f}\")\n", + "print(f\"Recall: {TSADMetric.Recall.value(ground_truth=test_labels, predict=anom_labels2):.4f}\")\n", + "print(f\"F1 Score: {TSADMetric.F1.value(ground_truth=test_labels, predict=anom_labels2):.4f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/v2.0.2/tutorials/forecast/0_ForecastIntro.html b/v2.0.2/tutorials/forecast/0_ForecastIntro.html new file mode 100644 index 000000000..4e8ce8285 --- /dev/null +++ b/v2.0.2/tutorials/forecast/0_ForecastIntro.html @@ -0,0 +1,609 @@ + + + + + + A Gentle Introduction to Forecasting in Merlion — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

A Gentle Introduction to Forecasting in Merlion

+

We begin by importing Merlion’s TimeSeries class and the data loader for the M4 dataset. We can then divide a specific time series from this dataset into training and testing splits.

+
+
[1]:
+
+
+
from merlion.utils import TimeSeries
+from ts_datasets.forecast import M4
+
+time_series, metadata = M4(subset="Hourly")[0]
+train_data = TimeSeries.from_pd(time_series[metadata.trainval])
+test_data = TimeSeries.from_pd(time_series[~metadata.trainval])
+
+
+
+

We can then initialize and train Merlion’s DefaultForecaster, which is an forecasting model that balances performance with efficiency. We also obtain its predictions on the test split.

+
+
[2]:
+
+
+
from merlion.models.defaults import DefaultForecasterConfig, DefaultForecaster
+model = DefaultForecaster(DefaultForecasterConfig())
+model.train(train_data=train_data)
+test_pred, test_err = model.forecast(time_stamps=test_data.time_stamps)
+
+
+
+
+
+
+
+
+Inferred granularity <Hour>
+/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/statsmodels/base/model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
+  warnings.warn("Maximum Likelihood optimization failed to "
+/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/statsmodels/base/model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
+  warnings.warn("Maximum Likelihood optimization failed to "
+/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/statsmodels/base/model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
+  warnings.warn("Maximum Likelihood optimization failed to "
+
+
+

Next, we visualize the model’s predictions.

+
+
[3]:
+
+
+
import matplotlib.pyplot as plt
+fig, ax = model.plot_forecast(time_series=test_data, plot_forecast_uncertainty=True)
+plt.show()
+
+
+
+
+
+
+
+../../_images/tutorials_forecast_0_ForecastIntro_6_0.png +
+
+

Finally, we quantitatively evaluate the model. sMAPE measures the error of the prediction on a scale of 0 to 100 (lower is better), while MSIS evaluates the quality of the 95% confidence band on a scale of 0 to 100 (lower is better).

+
+
[4]:
+
+
+
from scipy.stats import norm
+from merlion.evaluate.forecast import ForecastMetric
+
+# Compute the sMAPE of the predictions (0 to 100, smaller is better)
+smape = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=test_pred)
+
+# Compute the MSIS of the model's 95% confidence interval (0 to 100, smaller is better)
+lb = TimeSeries.from_pd(test_pred.to_pd() + norm.ppf(0.025) * test_err.to_pd().values)
+ub = TimeSeries.from_pd(test_pred.to_pd() + norm.ppf(0.975) * test_err.to_pd().values)
+msis = ForecastMetric.MSIS.value(ground_truth=test_data, predict=test_pred,
+                                 insample=train_data, lb=lb, ub=ub)
+print(f"sMAPE: {smape:.4f}, MSIS: {msis:.4f}")
+
+
+
+
+
+
+
+
+sMAPE: 4.1944, MSIS: 18.9331
+
+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/forecast/0_ForecastIntro.ipynb b/v2.0.2/tutorials/forecast/0_ForecastIntro.ipynb new file mode 100644 index 000000000..70837750e --- /dev/null +++ b/v2.0.2/tutorials/forecast/0_ForecastIntro.ipynb @@ -0,0 +1,159 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e4811d9d", + "metadata": {}, + "source": [ + "# A Gentle Introduction to Forecasting in Merlion" + ] + }, + { + "cell_type": "markdown", + "id": "4ed1853a", + "metadata": {}, + "source": [ + "We begin by importing Merlion's `TimeSeries` class and the data loader for the `M4` dataset. We can then divide a specific time series from this dataset into training and testing splits." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "382ff20e", + "metadata": {}, + "outputs": [], + "source": [ + "from merlion.utils import TimeSeries\n", + "from ts_datasets.forecast import M4\n", + "\n", + "time_series, metadata = M4(subset=\"Hourly\")[0]\n", + "train_data = TimeSeries.from_pd(time_series[metadata.trainval])\n", + "test_data = TimeSeries.from_pd(time_series[~metadata.trainval])" + ] + }, + { + "cell_type": "markdown", + "id": "8d40994a", + "metadata": {}, + "source": [ + "We can then initialize and train Merlion's `DefaultForecaster`, which is an forecasting model that balances performance with efficiency. We also obtain its predictions on the test split." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "96f8384e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Inferred granularity \n", + "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/statsmodels/base/model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " warnings.warn(\"Maximum Likelihood optimization failed to \"\n", + "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/statsmodels/base/model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " warnings.warn(\"Maximum Likelihood optimization failed to \"\n", + "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/statsmodels/base/model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " warnings.warn(\"Maximum Likelihood optimization failed to \"\n" + ] + } + ], + "source": [ + "from merlion.models.defaults import DefaultForecasterConfig, DefaultForecaster\n", + "model = DefaultForecaster(DefaultForecasterConfig())\n", + "model.train(train_data=train_data)\n", + "test_pred, test_err = model.forecast(time_stamps=test_data.time_stamps)" + ] + }, + { + "cell_type": "markdown", + "id": "75f945ea", + "metadata": {}, + "source": [ + "Next, we visualize the model's predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f56890b3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = model.plot_forecast(time_series=test_data, plot_forecast_uncertainty=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bc51ef7e", + "metadata": {}, + "source": [ + "Finally, we quantitatively evaluate the model. sMAPE measures the error of the prediction on a scale of 0 to 100 (lower is better), while MSIS evaluates the quality of the 95% confidence band on a scale of 0 to 100 (lower is better)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "95e70579", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sMAPE: 4.1944, MSIS: 18.9331\n" + ] + } + ], + "source": [ + "from scipy.stats import norm\n", + "from merlion.evaluate.forecast import ForecastMetric\n", + "\n", + "# Compute the sMAPE of the predictions (0 to 100, smaller is better)\n", + "smape = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=test_pred)\n", + "\n", + "# Compute the MSIS of the model's 95% confidence interval (0 to 100, smaller is better)\n", + "lb = TimeSeries.from_pd(test_pred.to_pd() + norm.ppf(0.025) * test_err.to_pd().values)\n", + "ub = TimeSeries.from_pd(test_pred.to_pd() + norm.ppf(0.975) * test_err.to_pd().values)\n", + "msis = ForecastMetric.MSIS.value(ground_truth=test_data, predict=test_pred,\n", + " insample=train_data, lb=lb, ub=ub)\n", + "print(f\"sMAPE: {smape:.4f}, MSIS: {msis:.4f}\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/v2.0.2/tutorials/forecast/1_ForecastFeatures.html b/v2.0.2/tutorials/forecast/1_ForecastFeatures.html new file mode 100644 index 000000000..49f7c6398 --- /dev/null +++ b/v2.0.2/tutorials/forecast/1_ForecastFeatures.html @@ -0,0 +1,1304 @@ + + + + + + How to Use Forecasters in Merlion — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

How to Use Forecasters in Merlion

+

This notebook will guide you through using all the key features of forecasters in Merlion. Specifically, we will explain

+
    +

  1. Initializing a forecasting model (including ensembles and automatic model selectors)

    2. +

  2. Training the model

    3. +

  3. Producing a forecast with the model

    4. +

  4. Visualizing the model’s predictions

    5. +

  5. Quantitatively evaluating the model

    6. +

  6. Saving and loading a trained model

    7. +

  7. Simulating the live deployment of a model using a ForecastEvaluator

    8. +
+

We will be using a single example time series for this whole notebook. We load it now:

+
+
[1]:
+
+
+
import matplotlib.pyplot as plt
+import numpy as np
+
+from merlion.utils.time_series import TimeSeries
+from ts_datasets.forecast import M4
+
+# Load the time series
+# time_series is a time-indexed pandas.DataFrame
+# trainval is a time-indexed pandas.Series indicating whether each timestamp is for training or testing
+time_series, metadata = M4(subset="Hourly")[5]
+trainval = metadata["trainval"]
+
+# Is there any missing data?
+timedeltas = np.diff(time_series.index)
+print(f"Has missing data: {any(timedeltas != timedeltas[0])}")
+
+# Visualize the time series and draw a dotted line to indicate the train/test split
+fig = plt.figure(figsize=(10, 6))
+ax = fig.add_subplot(111)
+ax.plot(time_series)
+ax.axvline(time_series[trainval].index[-1], ls="--", lw="2", c="k")
+plt.show()
+
+# Split the time series into train/test splits, and convert it to Merlion format
+train_data = TimeSeries.from_pd(time_series[trainval])
+test_data  = TimeSeries.from_pd(time_series[~trainval])
+print(f"{len(train_data)} points in train split, "
+      f"{len(test_data)} points in test split.")
+
+
+
+
+
+
+
+
+Has missing data: False
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+
+
+
+
+
+../../_images/tutorials_forecast_1_ForecastFeatures_1_1.png +
+
+
+
+
+
+
+700 points in train split, 48 points in test split.
+
+
+
+

Model Initialization

+

In this notebook, we will use three different forecasting models: 1. ARIMA (a classic stochastic process model) 2. Prophet (Facebook’s popular time series forecasting model) 3. MSES (the Multi-Scale Exponential Smoothing model, developed in-house)

+

Let’s start by initializing each of them.

+
+
[2]:
+
+
+
# Import models & configs
+from merlion.models.forecast.arima import Arima, ArimaConfig
+from merlion.models.forecast.prophet import Prophet, ProphetConfig
+from merlion.models.forecast.smoother import MSES, MSESConfig
+
+# Import data pre-processing transforms
+from merlion.transform.base import Identity
+from merlion.transform.resample import TemporalResample
+
+# All models are initialized using the syntax ModelClass(config),
+# where config is a model-specific configuration object. This is where
+# you specify any algorithm-specific hyperparameters, as well as any
+# data pre-processing transforms.
+
+# ARIMA assumes that input data is sampled at a regular interval,
+# so we set its transform to resample at that interval. We must also specify
+# a maximum prediction horizon.
+config1 = ArimaConfig(max_forecast_steps=100, order=(20, 1, 5),
+                      transform=TemporalResample(granularity="1h"))
+model1  = Arima(config1)
+
+
+# Prophet has no real assumptions on the input data (and doesn't require
+# a maximum prediction horizon), so we skip data pre-processing by using
+# the Identity transform.
+config2 = ProphetConfig(max_forecast_steps=None, transform=Identity())
+model2  = Prophet(config2)
+
+
+# MSES assumes that the input data is sampled at a regular interval,
+# and requires us to specify a maximum prediction horizon. We will
+# also specify its look-back hyperparameter to be 60 here
+config3 = MSESConfig(max_forecast_steps=100, max_backstep=60,
+                     transform=TemporalResample(granularity="1h"))
+model3  = MSES(config3)
+
+
+
+

Now that we have initialized the individual models, we will also combine them in two different ensembles: ensemble simply takes the mean prediction of each individual model, and selector selects the best individual model based on its sMAPE (symmetric Mean Average Precision Error). The sMAPE is a metric used to evaluate the quality of a continuous forecast. For ground truth \(y \in \mathbb{R}^T\) and prediction \(\hat{y} \in \mathbb{R}^T\), the sMAPE is computed as

+
+\[\mathrm{sMAPE}(y, \hat{y}) = \frac{200}{T} \sum_{t = 1}^{T} \frac{\lvert \hat{y}_t - y_t \rvert}{\lvert\hat{y}_t\rvert + \lvert y_t \rvert}\]
+
+
[3]:
+
+
+
from merlion.evaluate.forecast import ForecastMetric
+from merlion.models.ensemble.combine import Mean, ModelSelector
+from merlion.models.ensemble.forecast import ForecasterEnsemble, ForecasterEnsembleConfig
+
+# The ForecasterEnsemble is a forecaster, and we treat it as a first-class model.
+# Its config takes a combiner object, specifying how you want to combine the
+# predictions of individual models in the ensemble. There are two ways to specify
+# the actual models in the ensemble, which we cover below.
+
+# The first way to specify the models in the ensemble is to provide them when
+# initializing the ForecasterEnsembleConfig.
+#
+# The combiner here will simply take the mean prediction of the ensembles here
+ensemble_config = ForecasterEnsembleConfig(
+    combiner=Mean(), models=[model1, model2, model3])
+ensemble = ForecasterEnsemble(config=ensemble_config)
+
+
+# Alternatively, you can directly specify the models when initializing the
+# ForecasterEnsemble itself.
+#
+# The combiner here uses the sMAPE to compare individual models, and
+# selects the model with the lowest sMAPE
+selector_config = ForecasterEnsembleConfig(
+    combiner=ModelSelector(metric=ForecastMetric.sMAPE))
+selector = ForecasterEnsemble(
+    config=selector_config, models=[model1, model2, model3])
+
+
+
+
+
+

Model Training

+

All forecasting models (and ensembles) share the same API for training. The train() method returns the model’s predictions and standard error of those predictions on the training data. Note that the standard error is just None if the model doesn’t support uncertainty estimation (this is the case for MSES and ensembles).

+
+
[4]:
+
+
+
print(f"Training {type(model1).__name__}...")
+forecast1, stderr1 = model1.train(train_data)
+
+print(f"\nTraining {type(model2).__name__}...")
+forecast2, stderr2 = model2.train(train_data)
+
+print(f"\nTraining {type(model3).__name__}...")
+forecast3, stderr3 = model3.train(train_data)
+
+print("\nTraining ensemble...")
+forecast_e, stderr_e = ensemble.train(train_data)
+
+print("\nTraining model selector...")
+forecast_s, stderr_s = selector.train(train_data)
+
+print("Done!")
+
+
+
+
+
+
+
+
+Training Arima...
+
+
+
+
+
+
+
+17:42:09 - cmdstanpy - INFO - Chain [1] start processing
+
+
+
+
+
+
+
+
+Training Prophet...
+
+
+
+
+
+
+
+17:42:09 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+
+Training MSES...
+
+Training ensemble...
+
+
+
+
+
+
+
+17:42:20 - cmdstanpy - INFO - Chain [1] start processing
+17:42:20 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+
+Training model selector...
+
+
+
+
+
+
+
+ForecastEvaluator: 100%|██████████| 500400/500400 [00:00<00:00, 3420724.07it/s]
+17:42:31 - cmdstanpy - INFO - Chain [1] start processing
+17:42:32 - cmdstanpy - INFO - Chain [1] done processing
+ForecastEvaluator: 100%|██████████| 500400/500400 [00:00<00:00, 1342155.82it/s]
+ForecastEvaluator: 100%|██████████| 500400/500400 [00:01<00:00, 407983.30it/s]
+
+
+
+
+
+
+
+Done!
+
+
+
+
+

Model Inference

+

To obtain a forecast from a trained model, we simply call model.forecast() with the Unix timestamps at which we the model to generate a forecast. In many cases, you may obtain these directly from a time series as shown below.

+
+
[5]:
+
+
+
# Truncate the test data to ensure that we are within each model's maximum
+# forecast horizon.
+sub_test_data = test_data[:50]
+
+# Obtain the time stamps corresponding to the test data
+time_stamps = sub_test_data.univariates[sub_test_data.names[0]].time_stamps
+
+# Get the forecast & standard error of each model. These are both
+# merlion.utils.TimeSeries objects. Note that the standard error is None for
+# models which don't support uncertainty estimation (like MSES and all
+# ensembles).
+forecast1, stderr1 = model1.forecast(time_stamps=time_stamps)
+forecast2, stderr2 = model2.forecast(time_stamps=time_stamps)
+
+# You may optionally specify a time series prefix as context. If one isn't
+# specified, the prefix is assumed to be the training data. Here, we just make
+# this dependence explicit. More generally, this feature is useful if you want
+# to use a pre-trained model to make predictions on data further in the future
+# from the last time it was trained.
+forecast3, stderr3 = model3.forecast(time_stamps=time_stamps, time_series_prev=train_data)
+
+# The same options are available for ensembles as well, though the stderr is None
+forecast_e, stderr_e = ensemble.forecast(time_stamps=time_stamps)
+forecast_s, stderr_s = selector.forecast(time_stamps=time_stamps, time_series_prev=train_data)
+
+
+
+
+
+

Model Visualization and Quantitative Evaluation

+

It is fairly transparent to visualize a model’s forecast and also quantitatively evaluate the forecast, using standard metrics like sMAPE. We show examples for all five models below.

+

Below, we quantitatively evaluate the models using the sMAPE metric. However, the ForecastMetric enum includes a number of other options as well. In general, you may use the syntax

+
ForecastMetric.<metric_name>.value(ground_truth=ground_truth, predict=forecast)
+
+
+

where <metric_name> is the name of the evaluation metric (see the API docs for details and more options), ground_truth is the original time series, and forecast is the forecast returned by the model. We show concrete examples with ForecastMetric.sMAPE below.

+
+
[6]:
+
+
+
from merlion.evaluate.forecast import ForecastMetric
+
+# We begin by computing the sMAPE of ARIMA's forecast (scale is 0 to 100)
+smape1 = ForecastMetric.sMAPE.value(ground_truth=sub_test_data,
+                                    predict=forecast1)
+print(f"{type(model1).__name__} sMAPE is {smape1:.3f}")
+
+# Next, we can visualize the actual forecast, and understand why it
+# attains this particular sMAPE. Since ARIMA supports uncertainty
+# estimation, we plot its error bars too.
+fig, ax = model1.plot_forecast(time_series=sub_test_data,
+                               plot_forecast_uncertainty=True)
+plt.show()
+
+
+
+
+
+
+
+
+Arima sMAPE is 3.472
+
+
+
+
+
+
+../../_images/tutorials_forecast_1_ForecastFeatures_11_1.png +
+
+
+
[7]:
+
+
+
# We begin by computing the sMAPE of Prophet's forecast (scale is 0 to 100)
+smape2 = ForecastMetric.sMAPE.value(sub_test_data, forecast2)
+print(f"{type(model2).__name__} sMAPE is {smape2:.3f}")
+
+# Next, we can visualize the actual forecast, and understand why it
+# attains this particular sMAPE. Since Prophet supports uncertainty
+# estimation, we plot its error bars too.
+# Note that we can specify time_series_prev here as well, though it
+# will not be visualized unless we also supply the keyword argument
+# plot_time_series_prev=True.
+fig, ax = model2.plot_forecast(time_series=sub_test_data,
+                               time_series_prev=train_data,
+                               plot_forecast_uncertainty=True)
+plt.show()
+
+
+
+
+
+
+
+
+Prophet sMAPE is 2.947
+
+
+
+
+
+
+../../_images/tutorials_forecast_1_ForecastFeatures_12_1.png +
+
+
+
[8]:
+
+
+
# We begin by computing the sMAPE of MSES's forecast (scale is 0 to 100)
+smape3 = ForecastMetric.sMAPE.value(sub_test_data, forecast3)
+print(f"{type(model3).__name__} sMAPE is {smape3:.3f}")
+
+# Next, we visualize the actual forecast, and understand why it
+# attains this particular sMAPE.
+fig, ax = model3.plot_forecast(time_series=sub_test_data,
+                               plot_forecast_uncertainty=True)
+plt.show()
+
+
+
+
+
+
+
+
+MSES sMAPE is 4.377
+
+
+
+
+
+
+../../_images/tutorials_forecast_1_ForecastFeatures_13_1.png +
+
+
+
[9]:
+
+
+
# Compute the sMAPE of the ensemble's forecast (scale is 0 to 100)
+smape_e = ForecastMetric.sMAPE.value(sub_test_data, forecast_e)
+print(f"Ensemble sMAPE is {smape_e:.3f}")
+
+# Visualize the forecast.
+fig, ax = ensemble.plot_forecast(time_series=sub_test_data,
+                                 plot_forecast_uncertainty=True)
+plt.show()
+
+
+
+
+
+
+
+
+Ensemble sMAPE is 2.505
+
+
+
+
+
+
+../../_images/tutorials_forecast_1_ForecastFeatures_14_1.png +
+
+
+
[10]:
+
+
+
# Compute the sMAPE of the selector's forecast (scale is 0 to 100)
+smape_s = ForecastMetric.sMAPE.value(sub_test_data, forecast_s)
+print(f"Selector sMAPE is {smape_s:.3f}")
+
+# Visualize the forecast.
+fig, ax = selector.plot_forecast(time_series=sub_test_data,
+                                 plot_forecast_uncertainty=True)
+plt.show()
+
+
+
+
+
+
+
+
+Selector sMAPE is 3.472
+
+
+
+
+
+
+../../_images/tutorials_forecast_1_ForecastFeatures_15_1.png +
+
+
+
+

Saving & Loading Models

+

All models have a save() method and load() class method. Models may also be loaded with the assistance of the ModelFactory, which works for arbitrary models. The save() method creates a new directory at the specified path, where it saves a json file representing the model’s config, as well as a binary file for the model’s state.

+

We will demonstrate these behaviors using our Prophet model (model2) for concreteness.

+
+
[11]:
+
+
+
import json
+import os
+import pprint
+from merlion.models.factory import ModelFactory
+
+# Save the model
+os.makedirs("models", exist_ok=True)
+path = os.path.join("models", "prophet")
+model2.save(path)
+
+# Print the config saved
+pp = pprint.PrettyPrinter()
+with open(os.path.join(path, "config.json")) as f:
+    print(f"{type(model2).__name__} Config")
+    pp.pprint(json.load(f))
+
+# Load the model using Prophet.load()
+model2_loaded = Prophet.load(dirname=path)
+
+# Load the model using the ModelFactory
+model2_factory_loaded = ModelFactory.load(name="Prophet", model_path=path)
+
+
+
+
+
+
+
+
+Prophet Config
+{'daily_seasonality': 'auto',
+ 'dim': 1,
+ 'exog_aggregation_policy': 'Mean',
+ 'exog_missing_value_policy': 'ZFill',
+ 'exog_transform': {'bias': None,
+                    'name': 'MeanVarNormalize',
+                    'normalize_bias': True,
+                    'normalize_scale': True,
+                    'scale': None},
+ 'holidays': None,
+ 'invert_transform': True,
+ 'max_forecast_steps': None,
+ 'seasonality_mode': 'additive',
+ 'target_seq_index': 0,
+ 'transform': {'name': 'Identity'},
+ 'uncertainty_samples': 100,
+ 'weekly_seasonality': 'auto',
+ 'yearly_seasonality': 'auto'}
+
+
+

We can do the same exact thing with ensembles! Note that the ensemble stores its underlying models in a nested structure. Additionally, the combiner (which is saved in the ForecasterEnsembleConfig), keeps track of the sMAPE achieved by each model (the metric_values key). This is all reflected in the config.

+
+
[12]:
+
+
+
# Save the selector
+path = os.path.join("models", "selector")
+selector.save(path)
+
+# Print the config saved. Note that we've saved all individual models,
+# and their paths are specified under the model_paths key.
+pp = pprint.PrettyPrinter()
+with open(os.path.join(path, "config.json")) as f:
+    print(f"Selector Config")
+    pp.pprint(json.load(f))
+
+# Load the selector
+selector_loaded = ForecasterEnsemble.load(dirname=path)
+
+# Load the selector using the ModelFactory
+selector_factory_loaded = ModelFactory.load(name="ForecasterEnsemble", model_path=path)
+
+
+
+
+
+
+
+
+Selector Config
+{'combiner': {'_override_models_used': {},
+              'abs_score': False,
+              'metric': 'ForecastMetric.sMAPE',
+              'metric_values': [5.3927462062042695,
+                                6.993034179559698,
+                                14.33041679538694],
+              'n_models': 3,
+              'name': 'ModelSelector'},
+ 'dim': 1,
+ 'exog_aggregation_policy': 'Mean',
+ 'exog_missing_value_policy': 'ZFill',
+ 'exog_transform': {'bias': None,
+                    'name': 'MeanVarNormalize',
+                    'normalize_bias': True,
+                    'normalize_scale': True,
+                    'scale': None},
+ 'invert_transform': True,
+ 'max_forecast_steps': None,
+ 'models': [{'dim': 1,
+             'exog_aggregation_policy': 'Mean',
+             'exog_missing_value_policy': 'ZFill',
+             'exog_transform': {'bias': None,
+                                'name': 'MeanVarNormalize',
+                                'normalize_bias': True,
+                                'normalize_scale': True,
+                                'scale': None},
+             'invert_transform': True,
+             'max_forecast_steps': 100,
+             'name': 'Arima',
+             'order': [20, 1, 5],
+             'target_seq_index': 0,
+             'transform': {'aggregation_policy': 'Mean',
+                           'granularity': 3600.0,
+                           'missing_value_policy': 'Interpolate',
+                           'name': 'TemporalResample',
+                           'origin': 0.0,
+                           'remove_non_overlapping': True,
+                           'trainable_granularity': False}},
+            {'daily_seasonality': 'auto',
+             'dim': 1,
+             'exog_aggregation_policy': 'Mean',
+             'exog_missing_value_policy': 'ZFill',
+             'exog_transform': {'bias': None,
+                                'name': 'MeanVarNormalize',
+                                'normalize_bias': True,
+                                'normalize_scale': True,
+                                'scale': None},
+             'holidays': None,
+             'invert_transform': True,
+             'max_forecast_steps': None,
+             'name': 'Prophet',
+             'seasonality_mode': 'additive',
+             'target_seq_index': 0,
+             'transform': {'name': 'Identity'},
+             'uncertainty_samples': 100,
+             'weekly_seasonality': 'auto',
+             'yearly_seasonality': 'auto'},
+            {'accel_weight': 1.0,
+             'dim': 1,
+             'eta': 0.0,
+             'inflation': 1.0,
+             'invert_transform': True,
+             'max_backstep': 60,
+             'max_forecast_steps': 100,
+             'name': 'MSES',
+             'optimize_acc': True,
+             'phi': 2.0,
+             'recency_weight': 0.5,
+             'rho': 0.0,
+             'target_seq_index': 0,
+             'transform': {'aggregation_policy': 'Mean',
+                           'granularity': 3600.0,
+                           'missing_value_policy': 'Interpolate',
+                           'name': 'TemporalResample',
+                           'origin': 0.0,
+                           'remove_non_overlapping': True,
+                           'trainable_granularity': False}}],
+ 'target_seq_index': 0,
+ 'transform': {'name': 'Identity'},
+ 'verbose': False}
+
+
+
+
+

Simulating Live Model Deployment

+

A typical model deployment scenario is as follows: 1. Train an initial model on some recent historical data 1. At a regular interval cadence, obtain the model’s forecast for a certain horizon 1. At a regular interval retrain_freq, retrain the entire model on the most recent data 1. Optionally, specify a maximum amount of data (train_window) that the model should use for training

+

We provide a ForecastEvaluator object which simulates the above deployment scenario, and also allows a user to evaluate the quality of the forecaster according to an evaluation metric of their choice. We illustrate two examples below, using ARIMA for the first example, and the model selector for the second.

+
+
[13]:
+
+
+
from merlion.evaluate.forecast import ForecastEvaluator, ForecastEvaluatorConfig, ForecastMetric
+
+def create_evaluator(model):
+    # Re-initialize the model, so we can re-train it from scratch
+    model.reset()
+
+    # Create an evaluation pipeline for the model, where we
+    # -- get the model's forecast every hour
+    # -- have the model forecast for a horizon of 6 hours
+    # -- re-train the model every 12 hours
+    # -- when we re-train the model, retrain it on only the past 2 weeks of data
+    evaluator = ForecastEvaluator(
+        model=model, config=ForecastEvaluatorConfig(
+            cadence="1h", horizon="6h", retrain_freq="12h", train_window="14d")
+    )
+    return evaluator
+
+
+
+

First, let’s evaluate ARIMA.

+
+
[14]:
+
+
+
# Obtain the results of running the evaluation pipeline for ARIMA.
+# These result objects are to be treated as a black box, and should be
+# passed directly to the evaluator's evaluate() method.
+model1_evaluator = create_evaluator(model1)
+model1_train_result, model1_test_result = model1_evaluator.get_predict(
+    train_vals=train_data, test_vals=test_data)
+
+
+
+
+
+
+
+
+ForecastEvaluator: 100%|██████████| 169200/169200 [00:12<00:00, 13919.39it/s]
+
+
+
+
[15]:
+
+
+
# Evaluate ARIMA's sMAPE and RMSE
+smape = model1_evaluator.evaluate(
+    ground_truth=test_data,
+    predict=model1_test_result,
+    metric=ForecastMetric.sMAPE)
+rmse  = model1_evaluator.evaluate(
+    ground_truth=test_data,
+    predict=model1_test_result,
+    metric=ForecastMetric.RMSE)
+print(f"{type(model1).__name__} sMAPE: {smape:.3f}")
+print(f"{type(model1).__name__} RMSE:  {rmse:.3f}")
+
+
+
+
+
+
+
+
+Arima sMAPE: 2.014
+Arima RMSE:  142.828
+
+
+

Next, we will evaluate the ensemble (taking the mean prediction of ARIMA, Prophet, and MSES every time the models are called).

+
+
[16]:
+
+
+
# Obtain the results of running the evaluation pipeline for the ensemble.
+# These result objects are to be treated as a black box, and should be
+# passed directly to the evaluator's evaluate() method.
+ensemble_evaluator = create_evaluator(ensemble)
+ensemble_train_result, ensemble_test_result = ensemble_evaluator.get_predict(
+    train_vals=train_data, test_vals=test_data)
+
+
+
+
+
+
+
+
+17:43:09 - cmdstanpy - INFO - Chain [1] start processing
+17:43:09 - cmdstanpy - INFO - Chain [1] done processing
+ForecastEvaluator:  26%|██▌       | 43200/169200 [00:09<00:29, 4298.97it/s]17:43:28 - cmdstanpy - INFO - Chain [1] start processing
+17:43:28 - cmdstanpy - INFO - Chain [1] done processing
+ForecastEvaluator:  51%|█████     | 86400/169200 [00:25<00:20, 3994.83it/s]17:43:43 - cmdstanpy - INFO - Chain [1] start processing
+17:43:43 - cmdstanpy - INFO - Chain [1] done processing
+ForecastEvaluator:  77%|███████▋  | 129600/169200 [00:40<00:09, 4108.92it/s]17:43:59 - cmdstanpy - INFO - Chain [1] start processing
+17:43:59 - cmdstanpy - INFO - Chain [1] done processing
+ForecastEvaluator: 100%|██████████| 169200/169200 [00:56<00:00, 2979.61it/s]
+
+
+
+
[17]:
+
+
+
# Evaluate the selector's sMAPE and RMSE
+smape = ensemble_evaluator.evaluate(
+    ground_truth=test_data,
+    predict=ensemble_test_result,
+    metric=ForecastMetric.sMAPE)
+rmse  = ensemble_evaluator.evaluate(
+    ground_truth=test_data,
+    predict=ensemble_test_result,
+    metric=ForecastMetric.RMSE)
+print(f"Ensemble sMAPE: {smape:.3f}")
+print(f"Ensemble RMSE:  {rmse:.3f}")
+
+
+
+
+
+
+
+
+Ensemble sMAPE: 2.914
+Ensemble RMSE:  211.616
+
+
+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
+ +
+
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+ + + +
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+ + + + +
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+ +
+
+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/forecast/1_ForecastFeatures.ipynb b/v2.0.2/tutorials/forecast/1_ForecastFeatures.ipynb new file mode 100644 index 000000000..ad29792ee --- /dev/null +++ b/v2.0.2/tutorials/forecast/1_ForecastFeatures.ipynb @@ -0,0 +1,901 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to Use Forecasters in Merlion\n", + "\n", + "This notebook will guide you through using all the key features of forecasters in Merlion. Specifically, we will explain\n", + "\n", + "1. Initializing a forecasting model (including ensembles and automatic model selectors)\n", + "1. Training the model\n", + "1. Producing a forecast with the model\n", + "1. Visualizing the model's predictions\n", + "1. Quantitatively evaluating the model\n", + "1. Saving and loading a trained model\n", + "1. Simulating the live deployment of a model using a `ForecastEvaluator`\n", + "\n", + "We will be using a single example time series for this whole notebook. We load it now:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Has missing data: False\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "700 points in train split, 48 points in test split.\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from merlion.utils.time_series import TimeSeries\n", + "from ts_datasets.forecast import M4\n", + "\n", + "# Load the time series\n", + "# time_series is a time-indexed pandas.DataFrame\n", + "# trainval is a time-indexed pandas.Series indicating whether each timestamp is for training or testing\n", + "time_series, metadata = M4(subset=\"Hourly\")[5]\n", + "trainval = metadata[\"trainval\"]\n", + "\n", + "# Is there any missing data?\n", + "timedeltas = np.diff(time_series.index)\n", + "print(f\"Has missing data: {any(timedeltas != timedeltas[0])}\")\n", + "\n", + "# Visualize the time series and draw a dotted line to indicate the train/test split\n", + "fig = plt.figure(figsize=(10, 6))\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(time_series)\n", + "ax.axvline(time_series[trainval].index[-1], ls=\"--\", lw=\"2\", c=\"k\")\n", + "plt.show()\n", + "\n", + "# Split the time series into train/test splits, and convert it to Merlion format\n", + "train_data = TimeSeries.from_pd(time_series[trainval])\n", + "test_data = TimeSeries.from_pd(time_series[~trainval])\n", + "print(f\"{len(train_data)} points in train split, \"\n", + " f\"{len(test_data)} points in test split.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Initialization\n", + "\n", + "In this notebook, we will use three different forecasting models:\n", + "1. ARIMA (a classic stochastic process model)\n", + "2. Prophet (Facebook's popular time series forecasting model)\n", + "3. MSES (the Multi-Scale Exponential Smoothing model, developed in-house)\n", + "\n", + "Let's start by initializing each of them." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import models & configs\n", + "from merlion.models.forecast.arima import Arima, ArimaConfig\n", + "from merlion.models.forecast.prophet import Prophet, ProphetConfig\n", + "from merlion.models.forecast.smoother import MSES, MSESConfig\n", + "\n", + "# Import data pre-processing transforms\n", + "from merlion.transform.base import Identity\n", + "from merlion.transform.resample import TemporalResample\n", + "\n", + "# All models are initialized using the syntax ModelClass(config),\n", + "# where config is a model-specific configuration object. This is where\n", + "# you specify any algorithm-specific hyperparameters, as well as any\n", + "# data pre-processing transforms.\n", + "\n", + "# ARIMA assumes that input data is sampled at a regular interval,\n", + "# so we set its transform to resample at that interval. We must also specify\n", + "# a maximum prediction horizon.\n", + "config1 = ArimaConfig(max_forecast_steps=100, order=(20, 1, 5),\n", + " transform=TemporalResample(granularity=\"1h\"))\n", + "model1 = Arima(config1)\n", + "\n", + "\n", + "# Prophet has no real assumptions on the input data (and doesn't require\n", + "# a maximum prediction horizon), so we skip data pre-processing by using\n", + "# the Identity transform.\n", + "config2 = ProphetConfig(max_forecast_steps=None, transform=Identity())\n", + "model2 = Prophet(config2)\n", + "\n", + "\n", + "# MSES assumes that the input data is sampled at a regular interval,\n", + "# and requires us to specify a maximum prediction horizon. We will\n", + "# also specify its look-back hyperparameter to be 60 here\n", + "config3 = MSESConfig(max_forecast_steps=100, max_backstep=60,\n", + " transform=TemporalResample(granularity=\"1h\"))\n", + "model3 = MSES(config3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have initialized the individual models, we will also combine them in two different ensembles: `ensemble` simply takes the mean prediction of each individual model, and `selector` selects the best individual model based on its sMAPE (symmetric Mean Average Precision Error). The sMAPE is a metric used to evaluate the quality of a continuous forecast. For ground truth $y \\in \\mathbb{R}^T$ and prediction $\\hat{y} \\in \\mathbb{R}^T$, the sMAPE is computed as\n", + "\n", + "$$\n", + "\\mathrm{sMAPE}(y, \\hat{y}) = \\frac{200}{T} \\sum_{t = 1}^{T} \\frac{\\lvert \\hat{y}_t - y_t \\rvert}{\\lvert\\hat{y}_t\\rvert + \\lvert y_t \\rvert}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from merlion.evaluate.forecast import ForecastMetric\n", + "from merlion.models.ensemble.combine import Mean, ModelSelector\n", + "from merlion.models.ensemble.forecast import ForecasterEnsemble, ForecasterEnsembleConfig\n", + "\n", + "# The ForecasterEnsemble is a forecaster, and we treat it as a first-class model.\n", + "# Its config takes a combiner object, specifying how you want to combine the \n", + "# predictions of individual models in the ensemble. There are two ways to specify\n", + "# the actual models in the ensemble, which we cover below.\n", + "\n", + "# The first way to specify the models in the ensemble is to provide them when\n", + "# initializing the ForecasterEnsembleConfig.\n", + "#\n", + "# The combiner here will simply take the mean prediction of the ensembles here\n", + "ensemble_config = ForecasterEnsembleConfig(\n", + " combiner=Mean(), models=[model1, model2, model3])\n", + "ensemble = ForecasterEnsemble(config=ensemble_config)\n", + "\n", + "\n", + "# Alternatively, you can directly specify the models when initializing the\n", + "# ForecasterEnsemble itself.\n", + "#\n", + "# The combiner here uses the sMAPE to compare individual models, and\n", + "# selects the model with the lowest sMAPE\n", + "selector_config = ForecasterEnsembleConfig(\n", + " combiner=ModelSelector(metric=ForecastMetric.sMAPE))\n", + "selector = ForecasterEnsemble(\n", + " config=selector_config, models=[model1, model2, model3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Training\n", + "\n", + "All forecasting models (and ensembles) share the same API for training. The `train()` method returns the model's predictions and standard error of those predictions on the training data. Note that the standard error is just `None` if the model doesn't support uncertainty estimation (this is the case for MSES and ensembles)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Arima...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:42:09 - cmdstanpy - INFO - Chain [1] start processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training Prophet...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:42:09 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training MSES...\n", + "\n", + "Training ensemble...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:42:20 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:42:20 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training model selector...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ForecastEvaluator: 100%|██████████| 500400/500400 [00:00<00:00, 3420724.07it/s]\n", + "17:42:31 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:42:32 - cmdstanpy - INFO - Chain [1] done processing\n", + "ForecastEvaluator: 100%|██████████| 500400/500400 [00:00<00:00, 1342155.82it/s]\n", + "ForecastEvaluator: 100%|██████████| 500400/500400 [00:01<00:00, 407983.30it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done!\n" + ] + } + ], + "source": [ + "print(f\"Training {type(model1).__name__}...\")\n", + "forecast1, stderr1 = model1.train(train_data)\n", + "\n", + "print(f\"\\nTraining {type(model2).__name__}...\")\n", + "forecast2, stderr2 = model2.train(train_data)\n", + "\n", + "print(f\"\\nTraining {type(model3).__name__}...\")\n", + "forecast3, stderr3 = model3.train(train_data)\n", + "\n", + "print(\"\\nTraining ensemble...\")\n", + "forecast_e, stderr_e = ensemble.train(train_data)\n", + "\n", + "print(\"\\nTraining model selector...\")\n", + "forecast_s, stderr_s = selector.train(train_data)\n", + "\n", + "print(\"Done!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Inference\n", + "\n", + "To obtain a forecast from a trained model, we simply call `model.forecast()` with the Unix timestamps at which we the model to generate a forecast. In many cases, you may obtain these directly from a time series as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Truncate the test data to ensure that we are within each model's maximum\n", + "# forecast horizon.\n", + "sub_test_data = test_data[:50]\n", + "\n", + "# Obtain the time stamps corresponding to the test data\n", + "time_stamps = sub_test_data.univariates[sub_test_data.names[0]].time_stamps\n", + "\n", + "# Get the forecast & standard error of each model. These are both\n", + "# merlion.utils.TimeSeries objects. Note that the standard error is None for\n", + "# models which don't support uncertainty estimation (like MSES and all\n", + "# ensembles).\n", + "forecast1, stderr1 = model1.forecast(time_stamps=time_stamps)\n", + "forecast2, stderr2 = model2.forecast(time_stamps=time_stamps)\n", + "\n", + "# You may optionally specify a time series prefix as context. If one isn't\n", + "# specified, the prefix is assumed to be the training data. Here, we just make\n", + "# this dependence explicit. More generally, this feature is useful if you want\n", + "# to use a pre-trained model to make predictions on data further in the future\n", + "# from the last time it was trained.\n", + "forecast3, stderr3 = model3.forecast(time_stamps=time_stamps, time_series_prev=train_data)\n", + "\n", + "# The same options are available for ensembles as well, though the stderr is None\n", + "forecast_e, stderr_e = ensemble.forecast(time_stamps=time_stamps)\n", + "forecast_s, stderr_s = selector.forecast(time_stamps=time_stamps, time_series_prev=train_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Visualization and Quantitative Evaluation\n", + "\n", + "It is fairly transparent to visualize a model's forecast and also quantitatively evaluate the forecast, using standard metrics like sMAPE. We show examples for all five models below.\n", + "\n", + "Below, we quantitatively evaluate the models using the sMAPE metric. However, the `ForecastMetric` enum includes a number of other options as well. In general, you may use the syntax\n", + "```\n", + "ForecastMetric..value(ground_truth=ground_truth, predict=forecast)\n", + "```\n", + "where `` is the name of the evaluation metric (see the API docs for details and more options), `ground_truth` is the original time series, and `forecast` is the forecast returned by the model. We show concrete examples with `ForecastMetric.sMAPE` below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arima sMAPE is 3.472\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from merlion.evaluate.forecast import ForecastMetric\n", + "\n", + "# We begin by computing the sMAPE of ARIMA's forecast (scale is 0 to 100)\n", + "smape1 = ForecastMetric.sMAPE.value(ground_truth=sub_test_data,\n", + " predict=forecast1)\n", + "print(f\"{type(model1).__name__} sMAPE is {smape1:.3f}\")\n", + "\n", + "# Next, we can visualize the actual forecast, and understand why it\n", + "# attains this particular sMAPE. Since ARIMA supports uncertainty\n", + "# estimation, we plot its error bars too.\n", + "fig, ax = model1.plot_forecast(time_series=sub_test_data,\n", + " plot_forecast_uncertainty=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prophet sMAPE is 2.947\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We begin by computing the sMAPE of Prophet's forecast (scale is 0 to 100)\n", + "smape2 = ForecastMetric.sMAPE.value(sub_test_data, forecast2)\n", + "print(f\"{type(model2).__name__} sMAPE is {smape2:.3f}\")\n", + "\n", + "# Next, we can visualize the actual forecast, and understand why it\n", + "# attains this particular sMAPE. Since Prophet supports uncertainty\n", + "# estimation, we plot its error bars too.\n", + "# Note that we can specify time_series_prev here as well, though it\n", + "# will not be visualized unless we also supply the keyword argument\n", + "# plot_time_series_prev=True.\n", + "fig, ax = model2.plot_forecast(time_series=sub_test_data,\n", + " time_series_prev=train_data,\n", + " plot_forecast_uncertainty=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSES sMAPE is 4.377\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We begin by computing the sMAPE of MSES's forecast (scale is 0 to 100)\n", + "smape3 = ForecastMetric.sMAPE.value(sub_test_data, forecast3)\n", + "print(f\"{type(model3).__name__} sMAPE is {smape3:.3f}\")\n", + "\n", + "# Next, we visualize the actual forecast, and understand why it \n", + "# attains this particular sMAPE.\n", + "fig, ax = model3.plot_forecast(time_series=sub_test_data,\n", + " plot_forecast_uncertainty=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ensemble sMAPE is 2.505\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute the sMAPE of the ensemble's forecast (scale is 0 to 100)\n", + "smape_e = ForecastMetric.sMAPE.value(sub_test_data, forecast_e)\n", + "print(f\"Ensemble sMAPE is {smape_e:.3f}\")\n", + "\n", + "# Visualize the forecast.\n", + "fig, ax = ensemble.plot_forecast(time_series=sub_test_data,\n", + " plot_forecast_uncertainty=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selector sMAPE is 3.472\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute the sMAPE of the selector's forecast (scale is 0 to 100)\n", + "smape_s = ForecastMetric.sMAPE.value(sub_test_data, forecast_s)\n", + "print(f\"Selector sMAPE is {smape_s:.3f}\")\n", + "\n", + "# Visualize the forecast.\n", + "fig, ax = selector.plot_forecast(time_series=sub_test_data,\n", + " plot_forecast_uncertainty=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving & Loading Models\n", + "\n", + "All models have a `save()` method and `load()` class method. Models may also be loaded with the assistance of the `ModelFactory`, which works for arbitrary models. The `save()` method creates a new directory at the specified path, where it saves a `json` file representing the model's config, as well as a binary file for the model's state.\n", + "\n", + "We will demonstrate these behaviors using our `Prophet` model (`model2`) for concreteness." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prophet Config\n", + "{'daily_seasonality': 'auto',\n", + " 'dim': 1,\n", + " 'exog_aggregation_policy': 'Mean',\n", + " 'exog_missing_value_policy': 'ZFill',\n", + " 'exog_transform': {'bias': None,\n", + " 'name': 'MeanVarNormalize',\n", + " 'normalize_bias': True,\n", + " 'normalize_scale': True,\n", + " 'scale': None},\n", + " 'holidays': None,\n", + " 'invert_transform': True,\n", + " 'max_forecast_steps': None,\n", + " 'seasonality_mode': 'additive',\n", + " 'target_seq_index': 0,\n", + " 'transform': {'name': 'Identity'},\n", + " 'uncertainty_samples': 100,\n", + " 'weekly_seasonality': 'auto',\n", + " 'yearly_seasonality': 'auto'}\n" + ] + } + ], + "source": [ + "import json\n", + "import os\n", + "import pprint\n", + "from merlion.models.factory import ModelFactory\n", + "\n", + "# Save the model\n", + "os.makedirs(\"models\", exist_ok=True)\n", + "path = os.path.join(\"models\", \"prophet\")\n", + "model2.save(path)\n", + "\n", + "# Print the config saved\n", + "pp = pprint.PrettyPrinter()\n", + "with open(os.path.join(path, \"config.json\")) as f:\n", + " print(f\"{type(model2).__name__} Config\")\n", + " pp.pprint(json.load(f))\n", + "\n", + "# Load the model using Prophet.load()\n", + "model2_loaded = Prophet.load(dirname=path)\n", + "\n", + "# Load the model using the ModelFactory\n", + "model2_factory_loaded = ModelFactory.load(name=\"Prophet\", model_path=path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can do the same exact thing with ensembles! Note that the ensemble stores its underlying models in a nested structure. Additionally, the combiner (which is saved in the `ForecasterEnsembleConfig`), keeps track of the sMAPE achieved by each model (the `metric_values` key). This is all reflected in the config." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selector Config\n", + "{'combiner': {'_override_models_used': {},\n", + " 'abs_score': False,\n", + " 'metric': 'ForecastMetric.sMAPE',\n", + " 'metric_values': [5.3927462062042695,\n", + " 6.993034179559698,\n", + " 14.33041679538694],\n", + " 'n_models': 3,\n", + " 'name': 'ModelSelector'},\n", + " 'dim': 1,\n", + " 'exog_aggregation_policy': 'Mean',\n", + " 'exog_missing_value_policy': 'ZFill',\n", + " 'exog_transform': {'bias': None,\n", + " 'name': 'MeanVarNormalize',\n", + " 'normalize_bias': True,\n", + " 'normalize_scale': True,\n", + " 'scale': None},\n", + " 'invert_transform': True,\n", + " 'max_forecast_steps': None,\n", + " 'models': [{'dim': 1,\n", + " 'exog_aggregation_policy': 'Mean',\n", + " 'exog_missing_value_policy': 'ZFill',\n", + " 'exog_transform': {'bias': None,\n", + " 'name': 'MeanVarNormalize',\n", + " 'normalize_bias': True,\n", + " 'normalize_scale': True,\n", + " 'scale': None},\n", + " 'invert_transform': True,\n", + " 'max_forecast_steps': 100,\n", + " 'name': 'Arima',\n", + " 'order': [20, 1, 5],\n", + " 'target_seq_index': 0,\n", + " 'transform': {'aggregation_policy': 'Mean',\n", + " 'granularity': 3600.0,\n", + " 'missing_value_policy': 'Interpolate',\n", + " 'name': 'TemporalResample',\n", + " 'origin': 0.0,\n", + " 'remove_non_overlapping': True,\n", + " 'trainable_granularity': False}},\n", + " {'daily_seasonality': 'auto',\n", + " 'dim': 1,\n", + " 'exog_aggregation_policy': 'Mean',\n", + " 'exog_missing_value_policy': 'ZFill',\n", + " 'exog_transform': {'bias': None,\n", + " 'name': 'MeanVarNormalize',\n", + " 'normalize_bias': True,\n", + " 'normalize_scale': True,\n", + " 'scale': None},\n", + " 'holidays': None,\n", + " 'invert_transform': True,\n", + " 'max_forecast_steps': None,\n", + " 'name': 'Prophet',\n", + " 'seasonality_mode': 'additive',\n", + " 'target_seq_index': 0,\n", + " 'transform': {'name': 'Identity'},\n", + " 'uncertainty_samples': 100,\n", + " 'weekly_seasonality': 'auto',\n", + " 'yearly_seasonality': 'auto'},\n", + " {'accel_weight': 1.0,\n", + " 'dim': 1,\n", + " 'eta': 0.0,\n", + " 'inflation': 1.0,\n", + " 'invert_transform': True,\n", + " 'max_backstep': 60,\n", + " 'max_forecast_steps': 100,\n", + " 'name': 'MSES',\n", + " 'optimize_acc': True,\n", + " 'phi': 2.0,\n", + " 'recency_weight': 0.5,\n", + " 'rho': 0.0,\n", + " 'target_seq_index': 0,\n", + " 'transform': {'aggregation_policy': 'Mean',\n", + " 'granularity': 3600.0,\n", + " 'missing_value_policy': 'Interpolate',\n", + " 'name': 'TemporalResample',\n", + " 'origin': 0.0,\n", + " 'remove_non_overlapping': True,\n", + " 'trainable_granularity': False}}],\n", + " 'target_seq_index': 0,\n", + " 'transform': {'name': 'Identity'},\n", + " 'verbose': False}\n" + ] + } + ], + "source": [ + "# Save the selector\n", + "path = os.path.join(\"models\", \"selector\")\n", + "selector.save(path)\n", + "\n", + "# Print the config saved. Note that we've saved all individual models,\n", + "# and their paths are specified under the model_paths key.\n", + "pp = pprint.PrettyPrinter()\n", + "with open(os.path.join(path, \"config.json\")) as f:\n", + " print(f\"Selector Config\")\n", + " pp.pprint(json.load(f))\n", + "\n", + "# Load the selector\n", + "selector_loaded = ForecasterEnsemble.load(dirname=path)\n", + "\n", + "# Load the selector using the ModelFactory\n", + "selector_factory_loaded = ModelFactory.load(name=\"ForecasterEnsemble\", model_path=path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating Live Model Deployment\n", + "\n", + "A typical model deployment scenario is as follows:\n", + "1. Train an initial model on some recent historical data\n", + "1. At a regular interval `cadence`, obtain the model's forecast for a certain `horizon`\n", + "1. At a regular interval `retrain_freq`, retrain the entire model on the most recent data\n", + "1. Optionally, specify a maximum amount of data (`train_window`) that the model should use for training\n", + "\n", + "We provide a `ForecastEvaluator` object which simulates the above deployment scenario, and also allows a user to evaluate the quality of the forecaster according to an evaluation metric of their choice. We illustrate two examples below, using ARIMA for the first example, and the model selector for the second." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from merlion.evaluate.forecast import ForecastEvaluator, ForecastEvaluatorConfig, ForecastMetric\n", + "\n", + "def create_evaluator(model):\n", + " # Re-initialize the model, so we can re-train it from scratch\n", + " model.reset()\n", + "\n", + " # Create an evaluation pipeline for the model, where we\n", + " # -- get the model's forecast every hour\n", + " # -- have the model forecast for a horizon of 6 hours\n", + " # -- re-train the model every 12 hours\n", + " # -- when we re-train the model, retrain it on only the past 2 weeks of data\n", + " evaluator = ForecastEvaluator(\n", + " model=model, config=ForecastEvaluatorConfig(\n", + " cadence=\"1h\", horizon=\"6h\", retrain_freq=\"12h\", train_window=\"14d\")\n", + " )\n", + " return evaluator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's evaluate ARIMA." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ForecastEvaluator: 100%|██████████| 169200/169200 [00:12<00:00, 13919.39it/s]\n" + ] + } + ], + "source": [ + "# Obtain the results of running the evaluation pipeline for ARIMA.\n", + "# These result objects are to be treated as a black box, and should be\n", + "# passed directly to the evaluator's evaluate() method.\n", + "model1_evaluator = create_evaluator(model1)\n", + "model1_train_result, model1_test_result = model1_evaluator.get_predict(\n", + " train_vals=train_data, test_vals=test_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arima sMAPE: 2.014\n", + "Arima RMSE: 142.828\n" + ] + } + ], + "source": [ + "# Evaluate ARIMA's sMAPE and RMSE\n", + "smape = model1_evaluator.evaluate(\n", + " ground_truth=test_data,\n", + " predict=model1_test_result,\n", + " metric=ForecastMetric.sMAPE)\n", + "rmse = model1_evaluator.evaluate(\n", + " ground_truth=test_data,\n", + " predict=model1_test_result,\n", + " metric=ForecastMetric.RMSE)\n", + "print(f\"{type(model1).__name__} sMAPE: {smape:.3f}\")\n", + "print(f\"{type(model1).__name__} RMSE: {rmse:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will evaluate the ensemble (taking the mean prediction of ARIMA, Prophet, and MSES every time the models are called)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:43:09 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:43:09 - cmdstanpy - INFO - Chain [1] done processing\n", + "ForecastEvaluator: 26%|██▌ | 43200/169200 [00:09<00:29, 4298.97it/s]17:43:28 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:43:28 - cmdstanpy - INFO - Chain [1] done processing\n", + "ForecastEvaluator: 51%|█████ | 86400/169200 [00:25<00:20, 3994.83it/s]17:43:43 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:43:43 - cmdstanpy - INFO - Chain [1] done processing\n", + "ForecastEvaluator: 77%|███████▋ | 129600/169200 [00:40<00:09, 4108.92it/s]17:43:59 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:43:59 - cmdstanpy - INFO - Chain [1] done processing\n", + "ForecastEvaluator: 100%|██████████| 169200/169200 [00:56<00:00, 2979.61it/s]\n" + ] + } + ], + "source": [ + "# Obtain the results of running the evaluation pipeline for the ensemble.\n", + "# These result objects are to be treated as a black box, and should be\n", + "# passed directly to the evaluator's evaluate() method.\n", + "ensemble_evaluator = create_evaluator(ensemble)\n", + "ensemble_train_result, ensemble_test_result = ensemble_evaluator.get_predict(\n", + " train_vals=train_data, test_vals=test_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ensemble sMAPE: 2.914\n", + "Ensemble RMSE: 211.616\n" + ] + } + ], + "source": [ + "# Evaluate the selector's sMAPE and RMSE\n", + "smape = ensemble_evaluator.evaluate(\n", + " ground_truth=test_data,\n", + " predict=ensemble_test_result,\n", + " metric=ForecastMetric.sMAPE)\n", + "rmse = ensemble_evaluator.evaluate(\n", + " ground_truth=test_data,\n", + " predict=ensemble_test_result,\n", + " metric=ForecastMetric.RMSE)\n", + "print(f\"Ensemble sMAPE: {smape:.3f}\")\n", + "print(f\"Ensemble RMSE: {rmse:.3f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/v2.0.2/tutorials/forecast/2_ForecastMultivariate.html b/v2.0.2/tutorials/forecast/2_ForecastMultivariate.html new file mode 100644 index 000000000..2de4998d4 --- /dev/null +++ b/v2.0.2/tutorials/forecast/2_ForecastMultivariate.html @@ -0,0 +1,752 @@ + + + + + + Multivariate Time Series Forecasting — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

Multivariate Time Series Forecasting

+

Multivariate time series forecasting works similarly to univariate time series forecasting (covered here and here). The main difference is that you must specify the index of a target univariate to forecast, e.g. for a 5-variable time series you may want to forecast the value of the 3rd variable (we specify this by indicating target_seq_index = 2). To begin, we will load the multivariate SeattleTrail dataset for time series +forecasting.

+
+
[1]:
+
+
+
from merlion.utils import TimeSeries
+from ts_datasets.forecast import SeattleTrail
+
+time_series, metadata = SeattleTrail()[0]
+train_data = TimeSeries.from_pd(time_series[metadata["trainval"]])
+test_data = TimeSeries.from_pd(time_series[~metadata["trainval"]])
+
+print(f"Time series is {train_data.dim}-dimensional")
+
+
+
+
+
+
+
+
+Time series is 5-dimensional
+
+
+
+

Model Initialization and Training

+

For the purposes of this tutorial, we will be using 3 models:

+
    +

  1. DefaultForeacster (which automatically detects whether the input time series is univariate or multivariate);

    2. +

  2. ARIMA (a classic univariate algorithm) trained to forecast a specific univariate; and

    3. +

  3. A ForecasterEnsemble which selects the better of the two models.

    4. +
+

All models are trained with a maximum allowed forecasting horizon of 100 steps. Note that all multivariate forecasting models can be used for univariate time series, and by specifying target_seq_index appropriately, univariate models can be used for multivariate time series as well. Moreover, the API is identical in all cases.

+
+
[2]:
+
+
+
from merlion.evaluate.forecast import ForecastMetric
+from merlion.models.factory import ModelFactory
+from merlion.models.ensemble.combine import ModelSelector
+from merlion.transform.resample import TemporalResample
+
+# Time series is sampled hourly, so max_forecast_steps = 24 means we can predict up to 1 day in the future
+target_seq_index = 2
+max_forecast_steps = 24
+kwargs = dict(target_seq_index=target_seq_index, max_forecast_steps=max_forecast_steps)
+
+model1 = ModelFactory.create("DefaultForecaster", **kwargs)
+model2 = ModelFactory.create("Arima", **kwargs)
+
+# This ModelSelector combiner picks the best model based on sMAPE
+model3 = ModelFactory.create("ForecasterEnsemble", models=[model1, model2], transform=TemporalResample(),
+                             combiner=ModelSelector(metric=ForecastMetric.sMAPE))
+for model in [model1, model2, model3]:
+    print(f"Training {type(model).__name__}...")
+    train_pred, train_stderr = model.train(train_data)
+
+
+
+
+
+
+
+
+Training DefaultForecaster...
+
+
+
+
+
+
+
+Inferred granularity 0 days 01:00:00
+Inferred granularity 0 days 01:00:00
+
+
+
+
+
+
+
+Training Arima...
+
+
+
+
+
+
+
+Inferred granularity 0 days 01:00:00
+
+
+
+
+
+
+
+Training ForecasterEnsemble...
+
+
+
+
+
+
+
+ForecastEvaluator: 100%|██████████| 31550400/31550400 [01:36<00:00, 328262.84it/s]
+ForecastEvaluator: 100%|██████████| 31550400/31550400 [03:24<00:00, 154110.26it/s]
+
+
+
+
+

Model Inference and Quantitative Evaluation

+

Like univariate models, we may call model.forecast() to get a forecast and potentially a standard error for the model. We can use these to evaluate the model’s performance. Note that the model selector successfully picks the better of the two models.

+
+
[3]:
+
+
+
from merlion.evaluate.forecast import ForecastMetric
+
+for model in [model1, model2, model3]:
+    forecast, stderr = model.forecast(test_data.time_stamps[:max_forecast_steps])
+    rmse = ForecastMetric.RMSE.value(ground_truth=test_data, predict=forecast, target_seq_index=target_seq_index)
+    smape = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=forecast, target_seq_index=target_seq_index)
+    print(f"{type(model).__name__}")
+    print(f"RMSE:  {rmse:.4f}")
+    print(f"sMAPE: {smape:.4f}")
+    print()
+
+
+
+
+
+
+
+
+DefaultForecaster
+RMSE:  7.5235
+sMAPE: 132.8147
+
+Arima
+RMSE:  10.2208
+sMAPE: 140.2771
+
+ForecasterEnsemble
+RMSE:  7.5235
+sMAPE: 132.8147
+
+
+
+

We can also use a ForecastEvaluator to evaluate a model in a manner that simulates live deployment. Here, we train an initial model on the training data, and we obtain its predictions on the training data using a sliding window of 1 day (horizon="1d" means that we want the model to predict 1 day in the future at each time step, and cadence="1d" means that we obtain a prediction from the model once per day). Note that we never actually re-train the model (retrain_freq=None).

+
+
[4]:
+
+
+
from merlion.evaluate.forecast import ForecastEvaluator, ForecastEvaluatorConfig
+
+for model in [model1, model2]:
+    print(f"{type(model).__name__} Sliding Window Evaluation")
+    evaluator = ForecastEvaluator(model=model, config=ForecastEvaluatorConfig(
+        horizon="1d", cadence="1d", retrain_freq=None))
+    train_result, test_pred = evaluator.get_predict(train_vals=train_data, test_vals=test_data)
+    rmse = evaluator.evaluate(ground_truth=test_data, predict=test_pred, metric=ForecastMetric.RMSE)
+    smape = evaluator.evaluate(ground_truth=test_data, predict=test_pred, metric=ForecastMetric.sMAPE)
+    print(f"RMSE:  {rmse:.4f}")
+    print(f"sMAPE: {smape:.4f}")
+    print()
+
+
+
+
+
+
+
+
+Inferred granularity 0 days 01:00:00
+
+
+
+
+
+
+
+DefaultForecaster Sliding Window Evaluation
+
+
+
+
+
+
+
+ForecastEvaluator: 100%|██████████| 31528800/31528800 [02:03<00:00, 255804.57it/s]
+Inferred granularity 0 days 01:00:00
+
+
+
+
+
+
+
+RMSE:  12.0339
+sMAPE: 99.4165
+
+Arima Sliding Window Evaluation
+
+
+
+
+
+
+
+ForecastEvaluator: 100%|██████████| 31528800/31528800 [04:19<00:00, 121688.66it/s]
+
+
+
+
+
+
+
+RMSE:  13.1032
+sMAPE: 112.2604
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/forecast/2_ForecastMultivariate.ipynb b/v2.0.2/tutorials/forecast/2_ForecastMultivariate.ipynb new file mode 100644 index 000000000..690fa21b6 --- /dev/null +++ b/v2.0.2/tutorials/forecast/2_ForecastMultivariate.ipynb @@ -0,0 +1,274 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bd943f7c", + "metadata": {}, + "source": [ + "# Multivariate Time Series Forecasting\n", + "\n", + "Multivariate time series forecasting works similarly to univariate time series forecasting (covered [here](0_ForecastIntro.ipynb) and [here](1_ForecastFeatures.ipynb)). The main difference is that you must specify the index of a target univariate to forecast, e.g. for a 5-variable time series you may want to forecast the value of the 3rd variable (we specify this by indicating `target_seq_index = 2`). To begin, we will load the multivariate `SeattleTrail` dataset for time series forecasting." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a6c1f175", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time series is 5-dimensional\n" + ] + } + ], + "source": [ + "from merlion.utils import TimeSeries\n", + "from ts_datasets.forecast import SeattleTrail\n", + "\n", + "time_series, metadata = SeattleTrail()[0]\n", + "train_data = TimeSeries.from_pd(time_series[metadata[\"trainval\"]])\n", + "test_data = TimeSeries.from_pd(time_series[~metadata[\"trainval\"]])\n", + "\n", + "print(f\"Time series is {train_data.dim}-dimensional\")" + ] + }, + { + "cell_type": "markdown", + "id": "82df16db", + "metadata": {}, + "source": [ + "## Model Initialization and Training\n", + "\n", + "For the purposes of this tutorial, we will be using 3 models:\n", + "\n", + "1. `DefaultForeacster` (which automatically detects whether the input time series is univariate or multivariate);\n", + "2. `ARIMA` (a classic univariate algorithm) trained to forecast a specific univariate; and \n", + "3. A `ForecasterEnsemble` which selects the better of the two models.\n", + "\n", + "All models are trained with a maximum allowed forecasting horizon of 100 steps. Note that all multivariate forecasting models can be used for univariate time series, and by specifying `target_seq_index` appropriately, univariate models can be used for multivariate time series as well. Moreover, the API is identical in all cases." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "46593ce3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training DefaultForecaster...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Inferred granularity 0 days 01:00:00\n", + "Inferred granularity 0 days 01:00:00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Arima...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Inferred granularity 0 days 01:00:00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training ForecasterEnsemble...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ForecastEvaluator: 100%|██████████| 31550400/31550400 [01:36<00:00, 328262.84it/s]\n", + "ForecastEvaluator: 100%|██████████| 31550400/31550400 [03:24<00:00, 154110.26it/s]\n" + ] + } + ], + "source": [ + "from merlion.evaluate.forecast import ForecastMetric\n", + "from merlion.models.factory import ModelFactory\n", + "from merlion.models.ensemble.combine import ModelSelector\n", + "from merlion.transform.resample import TemporalResample\n", + "\n", + "# Time series is sampled hourly, so max_forecast_steps = 24 means we can predict up to 1 day in the future\n", + "target_seq_index = 2\n", + "max_forecast_steps = 24\n", + "kwargs = dict(target_seq_index=target_seq_index, max_forecast_steps=max_forecast_steps)\n", + "\n", + "model1 = ModelFactory.create(\"DefaultForecaster\", **kwargs)\n", + "model2 = ModelFactory.create(\"Arima\", **kwargs)\n", + "\n", + "# This ModelSelector combiner picks the best model based on sMAPE\n", + "model3 = ModelFactory.create(\"ForecasterEnsemble\", models=[model1, model2], transform=TemporalResample(),\n", + " combiner=ModelSelector(metric=ForecastMetric.sMAPE))\n", + "for model in [model1, model2, model3]:\n", + " print(f\"Training {type(model).__name__}...\")\n", + " train_pred, train_stderr = model.train(train_data)" + ] + }, + { + "cell_type": "markdown", + "id": "a720d718", + "metadata": {}, + "source": [ + "## Model Inference and Quantitative Evaluation\n", + "Like univariate models, we may call `model.forecast()` to get a forecast and potentially a standard error for the model. We can use these to evaluate the model's performance. Note that the model selector successfully picks the better of the two models." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6ee7d7bd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DefaultForecaster\n", + "RMSE: 7.5235\n", + "sMAPE: 132.8147\n", + "\n", + "Arima\n", + "RMSE: 10.2208\n", + "sMAPE: 140.2771\n", + "\n", + "ForecasterEnsemble\n", + "RMSE: 7.5235\n", + "sMAPE: 132.8147\n", + "\n" + ] + } + ], + "source": [ + "from merlion.evaluate.forecast import ForecastMetric\n", + "\n", + "for model in [model1, model2, model3]:\n", + " forecast, stderr = model.forecast(test_data.time_stamps[:max_forecast_steps])\n", + " rmse = ForecastMetric.RMSE.value(ground_truth=test_data, predict=forecast, target_seq_index=target_seq_index)\n", + " smape = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=forecast, target_seq_index=target_seq_index)\n", + " print(f\"{type(model).__name__}\")\n", + " print(f\"RMSE: {rmse:.4f}\")\n", + " print(f\"sMAPE: {smape:.4f}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "id": "0df4307b", + "metadata": {}, + "source": [ + "We can also use a `ForecastEvaluator` to evaluate a model in a manner that simulates live deployment. Here, we train an initial model on the training data, and we obtain its predictions on the training data using a sliding window of 1 day (`horizon=\"1d\"` means that we want the model to predict 1 day in the future at each time step, and `cadence=\"1d\"` means that we obtain a prediction from the model once per day). Note that we never actually re-train the model (`retrain_freq=None`)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bcac94ff", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Inferred granularity 0 days 01:00:00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DefaultForecaster Sliding Window Evaluation\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ForecastEvaluator: 100%|██████████| 31528800/31528800 [02:03<00:00, 255804.57it/s]\n", + "Inferred granularity 0 days 01:00:00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 12.0339\n", + "sMAPE: 99.4165\n", + "\n", + "Arima Sliding Window Evaluation\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ForecastEvaluator: 100%|██████████| 31528800/31528800 [04:19<00:00, 121688.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 13.1032\n", + "sMAPE: 112.2604\n", + "\n" + ] + } + ], + "source": [ + "from merlion.evaluate.forecast import ForecastEvaluator, ForecastEvaluatorConfig\n", + "\n", + "for model in [model1, model2]:\n", + " print(f\"{type(model).__name__} Sliding Window Evaluation\")\n", + " evaluator = ForecastEvaluator(model=model, config=ForecastEvaluatorConfig(\n", + " horizon=\"1d\", cadence=\"1d\", retrain_freq=None))\n", + " train_result, test_pred = evaluator.get_predict(train_vals=train_data, test_vals=test_data)\n", + " rmse = evaluator.evaluate(ground_truth=test_data, predict=test_pred, metric=ForecastMetric.RMSE)\n", + " smape = evaluator.evaluate(ground_truth=test_data, predict=test_pred, metric=ForecastMetric.sMAPE)\n", + " print(f\"RMSE: {rmse:.4f}\")\n", + " print(f\"sMAPE: {smape:.4f}\")\n", + " print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/v2.0.2/tutorials/forecast/3_ForecastExogenous.html b/v2.0.2/tutorials/forecast/3_ForecastExogenous.html new file mode 100644 index 000000000..04f132feb --- /dev/null +++ b/v2.0.2/tutorials/forecast/3_ForecastExogenous.html @@ -0,0 +1,1065 @@ + + + + + + Forecasting With Exogenous Regressors — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

Forecasting With Exogenous Regressors

+

Consider a multivariate time series \(X^{(1)}, \ldots, X^{(t)}\), where each \(X^{(i)} \in \mathbb{R}^d\) is a d-dimensional vector. In multivariate forecasting, our goal is to predict the future values of the k’th univariate \(X_k^{(t+1)}, \ldots, X_k^{(t+h)}\).

+

Exogenous regressors \(Y^{(i)}\) are a set of additional variables whose values we know a priori. The task of forecasting with exogenous regressors is to predict our target univariate \(X_k^{(t+1)}, \ldots, X_k^{(t+h)}\), conditioned on - The past values of the time series \(X^{(1)}, \ldots, X^{(t)}\) - The past values of the exogenous regressors \(Y^{(1)}, \ldots, Y^{(t)}\) - The future values of the exogenous regressors \(Y^{(t+1)}, \ldots, Y^{(t+h)}\)

+

For example, one can consider the task of predicting the sales of a specific item at a store. Endogenous variables \(X^{(i)} \in \mathbb{R}^4\) may contain the number of units sold (the target univariate), the temperature outside, the consumer price index, and the current unemployemnt rate. Exogenous variables \(Y^{(i)} \in \mathbb{R}^6\) are variables that the store has control over or prior knowledge of. They may include whether a particular day is a holiday, and various information +about the sort of markdowns the store is running.

+

To be more concrete, let’s show this with some real data.

+
+
[1]:
+
+
+
# This is the same dataset used in the custom dataset tutorial
+import os
+from ts_datasets.forecast import CustomDataset
+csv = os.path.join("..", "..", "data", "walmart", "walmart_mini.csv")
+dataset = CustomDataset(rootdir=csv, index_cols=["Store", "Dept"], test_frac=0.10)
+ts, md = dataset[-1]
+display(ts)
+
+
+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Weekly_SalesTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemploymentIsHoliday
Date
2010-02-0539602.4740.192.572NaNNaNNaNNaNNaN210.7526058.324False
2010-02-1237984.4438.492.548NaNNaNNaNNaNNaN210.8979948.324True
2010-02-1938889.4339.692.514NaNNaNNaNNaNNaN210.9451608.324False
2010-02-2641137.7446.102.561NaNNaNNaNNaNNaN210.9759578.324False
2010-03-0539883.5047.172.625NaNNaNNaNNaNNaN211.0067548.324False
....................................
2012-09-2837104.6779.453.6667106.051.911.651549.103946.03222.6164336.565False
2012-10-0536361.2870.273.6176037.76NaN10.043027.373853.40222.8159306.170False
2012-10-1235332.3460.973.6012145.50NaN33.31586.8310421.01223.0154266.170False
2012-10-1935721.0968.083.5944461.89NaN1.141579.672642.29223.0598086.170False
2012-10-2634260.7669.793.5066152.59129.77200.00272.292924.15223.0783376.170False
+

143 rows × 11 columns

+
+
+
+
[2]:
+
+
+
from merlion.utils import TimeSeries
+
+# Get the endogenous variables X and split them into train & test
+endog = ts[["Weekly_Sales", "Temperature", "CPI", "Unemployment"]]
+train = TimeSeries.from_pd(endog[md.trainval])
+test = TimeSeries.from_pd(endog[~md.trainval])
+
+# Get the exogenous variables Y
+exog = TimeSeries.from_pd(ts[["IsHoliday", "MarkDown1", "MarkDown2", "MarkDown3", "MarkDown4", "MarkDown5"]])
+
+
+
+
+
+
+
+
+The earliest univariate starts at 2010-02-05 00:00:00, but the latest univariate starts at 2011-11-11 00:00:00, a difference of 644 days 00:00:00. This is more than 10% of the length of the shortest univariate (350 days 00:00:00). You may want to check that the univariates cover the same window of time.
+Stack (most recent call last):
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 197, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 87, in _run_code
+    exec(code, run_globals)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel_launcher.py", line 16, in <module>
+    app.launch_new_instance()
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/traitlets/config/application.py", line 845, in launch_instance
+    app.start()
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/kernelapp.py", line 612, in start
+    self.io_loop.start()
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 199, in start
+    self.asyncio_loop.run_forever()
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py", line 596, in run_forever
+    self._run_once()
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py", line 1890, in _run_once
+    handle._run()
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/events.py", line 80, in _run
+    self._context.run(self._callback, *self._args)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/ioloop.py", line 688, in <lambda>
+    lambda f: self._run_callback(functools.partial(callback, future))
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/ioloop.py", line 741, in _run_callback
+    ret = callback()
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py", line 814, in inner
+    self.ctx_run(self.run)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py", line 775, in run
+    yielded = self.gen.send(value)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/kernelbase.py", line 374, in dispatch_queue
+    yield self.process_one()
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py", line 250, in wrapper
+    runner = Runner(ctx_run, result, future, yielded)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py", line 741, in __init__
+    self.ctx_run(self.run)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py", line 775, in run
+    yielded = self.gen.send(value)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/kernelbase.py", line 358, in process_one
+    yield gen.maybe_future(dispatch(*args))
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py", line 234, in wrapper
+    yielded = ctx_run(next, result)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/kernelbase.py", line 261, in dispatch_shell
+    yield gen.maybe_future(handler(stream, idents, msg))
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py", line 234, in wrapper
+    yielded = ctx_run(next, result)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/kernelbase.py", line 536, in execute_request
+    self.do_execute(
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py", line 234, in wrapper
+    yielded = ctx_run(next, result)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/ipkernel.py", line 302, in do_execute
+    res = shell.run_cell(code, store_history=store_history, silent=silent)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/zmqshell.py", line 539, in run_cell
+    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 2898, in run_cell
+    result = self._run_cell(
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 2944, in _run_cell
+    return runner(coro)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/core/async_helpers.py", line 68, in _pseudo_sync_runner
+    coro.send(None)
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3169, in run_cell_async
+    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3361, in run_ast_nodes
+    if (await self.run_code(code, result,  async_=asy)):
+  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code
+    exec(code_obj, self.user_global_ns, self.user_ns)
+  File "<ipython-input-2-f4b6cbd5939f>", line 9, in <module>
+    exog = TimeSeries.from_pd(ts[["IsHoliday", "MarkDown1", "MarkDown2", "MarkDown3", "MarkDown4", "MarkDown5"]])
+  File "/Users/abhatnagar/Desktop/Merlion/merlion/utils/time_series.py", line 794, in from_pd
+    return cls(df=df, freq=freq, check_aligned=check_aligned)
+  File "/Users/abhatnagar/Desktop/Merlion/merlion/utils/time_series.py", line 493, in __init__
+    logger.warning(
+
+
+

Here, our task is to predict the weekly sales. We would like our model to also account for variables which may have an impact on consumer demand (i.e. temperature, consumer price index, and unemployment), as knowledge of these variables could improve the quality of our sales forecast. This would be a multivariate forecasting problem, covered here.

+

In principle, we could add markdowns and holidays to the multivariate model. However, as a retailer, we know a priori which days are holidays, and we ourselves control the markdowns. In many cases, we can get better forecasts by providing the future values of these variables in addition to the past values. Moreover, we may wish to model how changing the future markdowns would change the future sales. This is why we should model these variables as exogenous regressors instead.

+

All Merlion forecasters support an API which accepts exogenous regressors at both training and inference time, though only some models actually support the feature. Using the feature is as easy as specifying an optional argument exog_data to both train() and forecast(). We show how to use the feature for the popular Prophet model below, and demonstrate that adding exogenous regressors can improve the quality of the forecast.

+
+
[3]:
+
+
+
from merlion.evaluate.forecast import ForecastMetric
+from merlion.models.forecast.prophet import Prophet, ProphetConfig
+
+# Train a model without exogenous data
+model = Prophet(ProphetConfig(target_seq_index=0))
+model.train(train)
+pred, err = model.forecast(test.time_stamps)
+smape = ForecastMetric.sMAPE.value(test, pred, target_seq_index=model.target_seq_index)
+print(f"sMAPE (w/o exog) = {smape:.2f}")
+
+# Train a model with exogenous data
+exog_model = Prophet(ProphetConfig(target_seq_index=0))
+exog_model.train(train, exog_data=exog)
+exog_pred, exog_err = exog_model.forecast(test.time_stamps, exog_data=exog)
+exog_smape = ForecastMetric.sMAPE.value(test, exog_pred, target_seq_index=exog_model.target_seq_index)
+print(f"sMAPE (w/ exog)  = {exog_smape:.2f}")
+
+
+
+
+
+
+
+
+17:50:59 - cmdstanpy - INFO - Chain [1] start processing
+17:50:59 - cmdstanpy - INFO - Chain [1] done processing
+17:50:59 - cmdstanpy - INFO - Chain [1] start processing
+17:50:59 - cmdstanpy - INFO - Chain [1] done processing
+
+
+
+
+
+
+
+sMAPE (w/o exog) = 3.98
+sMAPE (w/ exog)  = 3.18
+
+
+

Before we wrap up this tutorial, we note that the exogenous variables contain a lot of missing data:

+
+
[4]:
+
+
+
display(exog.to_pd())
+
+
+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
IsHolidayMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5
Date
2010-02-05FalseNaNNaNNaNNaNNaN
2010-02-12TrueNaNNaNNaNNaNNaN
2010-02-19FalseNaNNaNNaNNaNNaN
2010-02-26FalseNaNNaNNaNNaNNaN
2010-03-05FalseNaNNaNNaNNaNNaN
.....................
2012-09-28False7106.051.911.651549.103946.03
2012-10-05False6037.76NaN10.043027.373853.40
2012-10-12False2145.50NaN33.31586.8310421.01
2012-10-19False4461.89NaN1.141579.672642.29
2012-10-26False6152.59129.77200.00272.292924.15
+

143 rows × 6 columns

+
+
+

Behind the scenes, Merlion models will apply an optional exog_transform to the exogenous variables, and they will then resample the exogenous variables to the same timestamps as the endogenous variables. This resampling is achieved using the exog_missing_value_policy and exog_aggregation_policy, which can be specified in the config of any model which accepts exogenous regressors. We can see the default values for each of these parameters by inspecting the config:

+
+
[5]:
+
+
+
print(f"Default exog_transform:            {type(exog_model.config.exog_transform).__name__}")
+print(f"Default exog_missing_value_policy: {exog_model.config.exog_missing_value_policy}")
+print(f"Default exog_aggregation_policy:   {exog_model.config.exog_aggregation_policy}")
+
+
+
+
+
+
+
+
+Default exog_transform:            MeanVarNormalize
+Default exog_missing_value_policy: MissingValuePolicy.ZFill
+Default exog_aggregation_policy:   AggregationPolicy.Mean
+
+
+

So in this case, we first apply mean-variance normalization to the exogenous data. Then, we impute missing values by filling them with zeros (ZFill), and we downsample the exogenous data by taking the Mean of any relevant windows.

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+ +
+
+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/forecast/3_ForecastExogenous.ipynb b/v2.0.2/tutorials/forecast/3_ForecastExogenous.ipynb new file mode 100644 index 000000000..0bb54fb3f --- /dev/null +++ b/v2.0.2/tutorials/forecast/3_ForecastExogenous.ipynb @@ -0,0 +1,689 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "42a0f481", + "metadata": {}, + "source": [ + "# Forecasting With Exogenous Regressors\n", + "Consider a multivariate time series $X^{(1)}, \\ldots, X^{(t)}$, where each $X^{(i)} \\in \\mathbb{R}^d$ is a d-dimensional vector. In multivariate forecasting, our goal is to predict the future values of the k'th univariate $X_k^{(t+1)}, \\ldots, X_k^{(t+h)}$. \n", + "\n", + "Exogenous regressors $Y^{(i)}$ are a set of additional variables whose values we know a priori. The task of forecasting with exogenous regressors is to predict our target univariate $X_k^{(t+1)}, \\ldots, X_k^{(t+h)}$, conditioned on\n", + "- The past values of the time series $X^{(1)}, \\ldots, X^{(t)}$\n", + "- The past values of the exogenous regressors $Y^{(1)}, \\ldots, Y^{(t)}$\n", + "- The *future* values of the exogenous regressors $Y^{(t+1)}, \\ldots, Y^{(t+h)}$\n", + "\n", + "For example, one can consider the task of predicting the sales of a specific item at a store. Endogenous variables $X^{(i)} \\in \\mathbb{R}^4$ may contain the number of units sold (the target univariate), the temperature outside, the consumer price index, and the current unemployemnt rate. Exogenous variables $Y^{(i)} \\in \\mathbb{R}^6$ are variables that the store has control over or prior knowledge of. They may include whether a particular day is a holiday, and various information about the sort of markdowns the store is running.\n", + "\n", + "To be more concrete, let's show this with some real data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "509b77ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Weekly_SalesTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemploymentIsHoliday
Date
2010-02-0539602.4740.192.572NaNNaNNaNNaNNaN210.7526058.324False
2010-02-1237984.4438.492.548NaNNaNNaNNaNNaN210.8979948.324True
2010-02-1938889.4339.692.514NaNNaNNaNNaNNaN210.9451608.324False
2010-02-2641137.7446.102.561NaNNaNNaNNaNNaN210.9759578.324False
2010-03-0539883.5047.172.625NaNNaNNaNNaNNaN211.0067548.324False
....................................
2012-09-2837104.6779.453.6667106.051.911.651549.103946.03222.6164336.565False
2012-10-0536361.2870.273.6176037.76NaN10.043027.373853.40222.8159306.170False
2012-10-1235332.3460.973.6012145.50NaN33.31586.8310421.01223.0154266.170False
2012-10-1935721.0968.083.5944461.89NaN1.141579.672642.29223.0598086.170False
2012-10-2634260.7669.793.5066152.59129.77200.00272.292924.15223.0783376.170False
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143 rows × 11 columns

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" + ], + "text/plain": [ + " Weekly_Sales Temperature Fuel_Price MarkDown1 MarkDown2 \\\n", + "Date \n", + "2010-02-05 39602.47 40.19 2.572 NaN NaN \n", + "2010-02-12 37984.44 38.49 2.548 NaN NaN \n", + "2010-02-19 38889.43 39.69 2.514 NaN NaN \n", + "2010-02-26 41137.74 46.10 2.561 NaN NaN \n", + "2010-03-05 39883.50 47.17 2.625 NaN NaN \n", + "... ... ... ... ... ... \n", + "2012-09-28 37104.67 79.45 3.666 7106.05 1.91 \n", + "2012-10-05 36361.28 70.27 3.617 6037.76 NaN \n", + "2012-10-12 35332.34 60.97 3.601 2145.50 NaN \n", + "2012-10-19 35721.09 68.08 3.594 4461.89 NaN \n", + "2012-10-26 34260.76 69.79 3.506 6152.59 129.77 \n", + "\n", + " MarkDown3 MarkDown4 MarkDown5 CPI Unemployment \\\n", + "Date \n", + "2010-02-05 NaN NaN NaN 210.752605 8.324 \n", + "2010-02-12 NaN NaN NaN 210.897994 8.324 \n", + "2010-02-19 NaN NaN NaN 210.945160 8.324 \n", + "2010-02-26 NaN NaN NaN 210.975957 8.324 \n", + "2010-03-05 NaN NaN NaN 211.006754 8.324 \n", + "... ... ... ... ... ... \n", + "2012-09-28 1.65 1549.10 3946.03 222.616433 6.565 \n", + "2012-10-05 10.04 3027.37 3853.40 222.815930 6.170 \n", + "2012-10-12 33.31 586.83 10421.01 223.015426 6.170 \n", + "2012-10-19 1.14 1579.67 2642.29 223.059808 6.170 \n", + "2012-10-26 200.00 272.29 2924.15 223.078337 6.170 \n", + "\n", + " IsHoliday \n", + "Date \n", + "2010-02-05 False \n", + "2010-02-12 True \n", + "2010-02-19 False \n", + "2010-02-26 False \n", + "2010-03-05 False \n", + "... ... \n", + "2012-09-28 False \n", + "2012-10-05 False \n", + "2012-10-12 False \n", + "2012-10-19 False \n", + "2012-10-26 False \n", + "\n", + "[143 rows x 11 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This is the same dataset used in the custom dataset tutorial\n", + "import os\n", + "from ts_datasets.forecast import CustomDataset\n", + "csv = os.path.join(\"..\", \"..\", \"data\", \"walmart\", \"walmart_mini.csv\")\n", + "dataset = CustomDataset(rootdir=csv, index_cols=[\"Store\", \"Dept\"], test_frac=0.10)\n", + "ts, md = dataset[-1]\n", + "display(ts)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f2ea8bed", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The earliest univariate starts at 2010-02-05 00:00:00, but the latest univariate starts at 2011-11-11 00:00:00, a difference of 644 days 00:00:00. This is more than 10% of the length of the shortest univariate (350 days 00:00:00). You may want to check that the univariates cover the same window of time.\n", + "Stack (most recent call last):\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py\", line 197, in _run_module_as_main\n", + " return _run_code(code, main_globals, None,\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py\", line 87, in _run_code\n", + " exec(code, run_globals)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel_launcher.py\", line 16, in \n", + " app.launch_new_instance()\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/traitlets/config/application.py\", line 845, in launch_instance\n", + " app.start()\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/kernelapp.py\", line 612, in start\n", + " self.io_loop.start()\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/platform/asyncio.py\", line 199, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py\", line 596, in run_forever\n", + " self._run_once()\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py\", line 1890, in _run_once\n", + " handle._run()\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/ioloop.py\", line 688, in \n", + " lambda f: self._run_callback(functools.partial(callback, future))\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/ioloop.py\", line 741, in _run_callback\n", + " ret = callback()\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py\", line 814, in inner\n", + " self.ctx_run(self.run)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py\", line 775, in run\n", + " yielded = self.gen.send(value)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 374, in dispatch_queue\n", + " yield self.process_one()\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py\", line 250, in wrapper\n", + " runner = Runner(ctx_run, result, future, yielded)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py\", line 741, in __init__\n", + " self.ctx_run(self.run)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py\", line 775, in run\n", + " yielded = self.gen.send(value)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 358, in process_one\n", + " yield gen.maybe_future(dispatch(*args))\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py\", line 234, in wrapper\n", + " yielded = ctx_run(next, result)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 261, in dispatch_shell\n", + " yield gen.maybe_future(handler(stream, idents, msg))\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py\", line 234, in wrapper\n", + " yielded = ctx_run(next, result)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 536, in execute_request\n", + " self.do_execute(\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/tornado/gen.py\", line 234, in wrapper\n", + " yielded = ctx_run(next, result)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/ipkernel.py\", line 302, in do_execute\n", + " res = shell.run_cell(code, store_history=store_history, silent=silent)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/ipykernel/zmqshell.py\", line 539, in run_cell\n", + " return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 2898, in run_cell\n", + " result = self._run_cell(\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 2944, in _run_cell\n", + " return runner(coro)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3169, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3361, in run_ast_nodes\n", + " if (await self.run_code(code, result, async_=asy)):\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3441, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"\", line 9, in \n", + " exog = TimeSeries.from_pd(ts[[\"IsHoliday\", \"MarkDown1\", \"MarkDown2\", \"MarkDown3\", \"MarkDown4\", \"MarkDown5\"]])\n", + " File \"/Users/abhatnagar/Desktop/Merlion/merlion/utils/time_series.py\", line 794, in from_pd\n", + " return cls(df=df, freq=freq, check_aligned=check_aligned)\n", + " File \"/Users/abhatnagar/Desktop/Merlion/merlion/utils/time_series.py\", line 493, in __init__\n", + " logger.warning(\n" + ] + } + ], + "source": [ + "from merlion.utils import TimeSeries\n", + "\n", + "# Get the endogenous variables X and split them into train & test\n", + "endog = ts[[\"Weekly_Sales\", \"Temperature\", \"CPI\", \"Unemployment\"]]\n", + "train = TimeSeries.from_pd(endog[md.trainval])\n", + "test = TimeSeries.from_pd(endog[~md.trainval])\n", + "\n", + "# Get the exogenous variables Y\n", + "exog = TimeSeries.from_pd(ts[[\"IsHoliday\", \"MarkDown1\", \"MarkDown2\", \"MarkDown3\", \"MarkDown4\", \"MarkDown5\"]])" + ] + }, + { + "cell_type": "markdown", + "id": "1b01c639", + "metadata": {}, + "source": [ + "Here, our task is to predict the weekly sales. We would like our model to also account for variables which may have an impact on consumer demand (i.e. temperature, consumer price index, and unemployment), as knowledge of these variables could improve the quality of our sales forecast. This would be a multivariate forecasting problem, covered [here](2_ForecastMultivariate.ipynb).\n", + "\n", + "In principle, we could add markdowns and holidays to the multivariate model. However, as a retailer, we know a priori which days are holidays, and we ourselves control the markdowns. In many cases, we can get better forecasts by providing the future values of these variables in addition to the past values. Moreover, we may wish to model how changing the future markdowns would change the future sales. This is why we should model these variables as exogenous regressors instead. \n", + "\n", + "All Merlion forecasters support an API which accepts exogenous regressors at both training and inference time, though only some models actually support the feature. Using the feature is as easy as specifying an optional argument `exog_data` to both `train()` and `forecast()`. We show how to use the feature for the popular `Prophet` model below, and demonstrate that adding exogenous regressors can improve the quality of the forecast." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "36f106f6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "17:50:59 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:50:59 - cmdstanpy - INFO - Chain [1] done processing\n", + "17:50:59 - cmdstanpy - INFO - Chain [1] start processing\n", + "17:50:59 - cmdstanpy - INFO - Chain [1] done processing\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sMAPE (w/o exog) = 3.98\n", + "sMAPE (w/ exog) = 3.18\n" + ] + } + ], + "source": [ + "from merlion.evaluate.forecast import ForecastMetric\n", + "from merlion.models.forecast.prophet import Prophet, ProphetConfig\n", + "\n", + "# Train a model without exogenous data\n", + "model = Prophet(ProphetConfig(target_seq_index=0))\n", + "model.train(train)\n", + "pred, err = model.forecast(test.time_stamps)\n", + "smape = ForecastMetric.sMAPE.value(test, pred, target_seq_index=model.target_seq_index)\n", + "print(f\"sMAPE (w/o exog) = {smape:.2f}\")\n", + "\n", + "# Train a model with exogenous data\n", + "exog_model = Prophet(ProphetConfig(target_seq_index=0))\n", + "exog_model.train(train, exog_data=exog)\n", + "exog_pred, exog_err = exog_model.forecast(test.time_stamps, exog_data=exog)\n", + "exog_smape = ForecastMetric.sMAPE.value(test, exog_pred, target_seq_index=exog_model.target_seq_index)\n", + "print(f\"sMAPE (w/ exog) = {exog_smape:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "39749b73", + "metadata": {}, + "source": [ + "Before we wrap up this tutorial, we note that the exogenous variables contain a lot of missing data:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5f2690f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IsHolidayMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5
Date
2010-02-05FalseNaNNaNNaNNaNNaN
2010-02-12TrueNaNNaNNaNNaNNaN
2010-02-19FalseNaNNaNNaNNaNNaN
2010-02-26FalseNaNNaNNaNNaNNaN
2010-03-05FalseNaNNaNNaNNaNNaN
.....................
2012-09-28False7106.051.911.651549.103946.03
2012-10-05False6037.76NaN10.043027.373853.40
2012-10-12False2145.50NaN33.31586.8310421.01
2012-10-19False4461.89NaN1.141579.672642.29
2012-10-26False6152.59129.77200.00272.292924.15
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143 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " IsHoliday MarkDown1 MarkDown2 MarkDown3 MarkDown4 MarkDown5\n", + "Date \n", + "2010-02-05 False NaN NaN NaN NaN NaN\n", + "2010-02-12 True NaN NaN NaN NaN NaN\n", + "2010-02-19 False NaN NaN NaN NaN NaN\n", + "2010-02-26 False NaN NaN NaN NaN NaN\n", + "2010-03-05 False NaN NaN NaN NaN NaN\n", + "... ... ... ... ... ... ...\n", + "2012-09-28 False 7106.05 1.91 1.65 1549.10 3946.03\n", + "2012-10-05 False 6037.76 NaN 10.04 3027.37 3853.40\n", + "2012-10-12 False 2145.50 NaN 33.31 586.83 10421.01\n", + "2012-10-19 False 4461.89 NaN 1.14 1579.67 2642.29\n", + "2012-10-26 False 6152.59 129.77 200.00 272.29 2924.15\n", + "\n", + "[143 rows x 6 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(exog.to_pd())" + ] + }, + { + "cell_type": "markdown", + "id": "b3c44fa9", + "metadata": {}, + "source": [ + "Behind the scenes, Merlion models will apply an optional `exog_transform` to the exogenous variables, and they will then resample the exogenous variables to the same timestamps as the endogenous variables. This resampling is achieved using the `exog_missing_value_policy` and `exog_aggregation_policy`, which can be specified in the config of any model which accepts exogenous regressors. We can see the default values for each of these parameters by inspecting the config:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f5a3707e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default exog_transform: MeanVarNormalize\n", + "Default exog_missing_value_policy: MissingValuePolicy.ZFill\n", + "Default exog_aggregation_policy: AggregationPolicy.Mean\n" + ] + } + ], + "source": [ + "print(f\"Default exog_transform: {type(exog_model.config.exog_transform).__name__}\")\n", + "print(f\"Default exog_missing_value_policy: {exog_model.config.exog_missing_value_policy}\")\n", + "print(f\"Default exog_aggregation_policy: {exog_model.config.exog_aggregation_policy}\")" + ] + }, + { + "cell_type": "markdown", + "id": "25b42747", + "metadata": {}, + "source": [ + "So in this case, we first apply mean-variance normalization to the exogenous data. Then, we impute missing values by filling them with zeros (`ZFill`), and we downsample the exogenous data by taking the `Mean` of any relevant windows." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/v2.0.2/tutorials/forecast/4_ForecastNewModel.html b/v2.0.2/tutorials/forecast/4_ForecastNewModel.html new file mode 100644 index 000000000..e12a649d1 --- /dev/null +++ b/v2.0.2/tutorials/forecast/4_ForecastNewModel.html @@ -0,0 +1,1011 @@ + + + + + + Adding a New Forecasting Model — Merlion 2.0.2 documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

Adding a New Forecasting Model

+

This notebook provides a minimal example on how to add a new forecasting model to Merlion. We follow the instructions in CONTRIBUTING.md. We suggest you review this notebook explaining how to use a Merlion forecasting model before reading this one.

+

More specifically, let’s implement a forecasting model whose forecast is just equal to the most recent observed value of the time series metric. For a more complete example, see our implementation of Sarima here.

+
+

Model Config Class

+

The first step of creating a new model is defining an appropriate config class, which inherits from ForecasterConfig:

+
+
[1]:
+
+
+
from merlion.models.forecast.base import ForecasterConfig
+
+class RepeatRecentConfig(ForecasterConfig):
+    def __init__(self, max_forecast_steps=None, **kwargs):
+        super().__init__(max_forecast_steps=max_forecast_steps, **kwargs)
+
+
+
+
+
+

Model Class

+

Next we define the model itself, which must inherit from the ForecasterBase base class and define all abstract methods. See the API docs for more details.

+
+
[2]:
+
+
+
from collections import OrderedDict
+from typing import List, Tuple
+
+import numpy as np
+import pandas as pd
+
+from merlion.models.forecast.base import ForecasterBase
+from merlion.utils.time_series import to_pd_datetime
+
+
+class RepeatRecent(ForecasterBase):
+    # The config class for RepeatRecent is RepeatRecentConfig, defined above
+    config_class = RepeatRecentConfig
+
+    @property
+    def require_even_sampling(self):
+        """
+        Many forecasters assume the input time series is sampled evenly.
+        That isn't a necessary assumption for this model, so override the property.
+        """
+        return False
+
+    def __init__(self, config):
+        """
+        Sets the model config and any other local variables. Here, we initialize
+        the most_recent_value to None.
+        """
+        super().__init__(config)
+        self.most_recent_value = None
+
+
+    def _train(self, train_data: pd.DataFrame, train_config=None) -> Tuple[pd.DataFrame, None]:
+        # "Train" the model. Here, we just gather the most recent values for each univariate.
+        self.most_recent_value = [(k, v.values[-1]) for k, v in train_data.items()]
+
+        # The model's "prediction" for the training data, is just the value from one step before.
+        pred = np.concatenate((np.zeros((1, self.dim)), train_data.values[:-1]))
+        train_forecast = pd.DataFrame(pred, index=train_data.index, columns=train_data.columns)
+
+        # This model doesn't have any notion of error
+        train_stderr = None
+
+        # Return the train prediction & standard error
+        return train_forecast, train_stderr
+
+    def _forecast(self, time_stamps: List[int],
+                  time_series_prev: pd.DataFrame = None,
+                  return_prev=False
+                ) -> Tuple[pd.DataFrame, None]:
+
+        # Use time_series_prev's most recent value if time_series_prev is given.
+        # Otherwise, use the most recent value stored from the training data
+        if time_series_prev is not None:
+            most_recent_value = [(k, v.values[-1]) for k, v in time_series_prev.items()]
+        else:
+            most_recent_value = self.most_recent_value
+
+        # The forecast is just the most recent value repeated for every upcoming timestamp.
+        # Note that we only care about the target_seq_index here.
+        i = self.target_seq_index
+        datetimes = to_pd_datetime(time_stamps)
+        name, val = most_recent_value[i]
+        forecast = pd.DataFrame([val] * len(datetimes), index=datetimes, columns=[name])
+
+        # If desired, pre-pend "predicted" vals of the target_seq_index of time_series_prev.
+        if return_prev and time_series_prev is not None:
+            pred = np.concatenate(([0], time_series_prev.values[:-1, i]))
+            prev_forecast = pd.DataFrame(pred, index=time_series_prev.index, columns=[name])
+            forecast = pd.concat((prev_forecast, forecast))
+
+        return forecast, None
+
+
+
+
+
+

Running the Model: A Simple Example

+

Let’s try running this model on some actual data! This next part assumes you’ve installed ts_datasets. We’ll begin by getting a time series from the M4 dataset & visualizing it.

+
+
[3]:
+
+
+
import matplotlib.pyplot as plt
+import pandas as pd
+
+from merlion.utils import TimeSeries, UnivariateTimeSeries
+from ts_datasets.forecast import M4
+
+time_series, metadata = M4(subset="Hourly")[0]
+
+# Visualize the full time series
+fig = plt.figure(figsize=(12, 6))
+ax = fig.add_subplot(111)
+ax.plot(time_series)
+
+# Label the train/test split with a dashed line
+ax.axvline(time_series[metadata["trainval"]].index[-1], ls="--", lw=2, c="k")
+
+plt.show()
+
+
+
+
+
+
+
+../../_images/tutorials_forecast_4_ForecastNewModel_6_0.png +
+
+

Now, we’ll split the data into train & test splits, and run our forecasting model on it.

+
+
[4]:
+
+
+
train_data = TimeSeries.from_pd(time_series[metadata["trainval"]])
+test_data  = TimeSeries.from_pd(time_series[~metadata["trainval"]])
+
+
+
+
+
[5]:
+
+
+
# Initialize a model & train it. The dataframe returned & printed
+# below is the model's "forecast" on the training data. None is
+# the uncertainty estimate.
+model = RepeatRecent(RepeatRecentConfig())
+model.train(train_data=train_data)
+
+
+
+
+
[5]:
+
+
+
+
+(                        H1
+ 1970-01-01 00:00:00    0.0
+ 1970-01-01 01:00:00  605.0
+ 1970-01-01 02:00:00  586.0
+ 1970-01-01 03:00:00  586.0
+ 1970-01-01 04:00:00  559.0
+ ...                    ...
+ 1970-01-29 23:00:00  820.0
+ 1970-01-30 00:00:00  790.0
+ 1970-01-30 01:00:00  784.0
+ 1970-01-30 02:00:00  752.0
+ 1970-01-30 03:00:00  739.0
+
+ [700 rows x 1 columns],
+ None)
+
+
+
+
[6]:
+
+
+
# Let's run our model on the test data now
+forecast, err = model.forecast(test_data.to_pd().index)
+print("Forecast")
+print(forecast)
+print()
+print("Error")
+print(err)
+
+
+
+
+
+
+
+
+Forecast
+                        H1
+1970-01-30 04:00:00  684.0
+1970-01-30 05:00:00  684.0
+1970-01-30 06:00:00  684.0
+1970-01-30 07:00:00  684.0
+1970-01-30 08:00:00  684.0
+1970-01-30 09:00:00  684.0
+1970-01-30 10:00:00  684.0
+1970-01-30 11:00:00  684.0
+1970-01-30 12:00:00  684.0
+1970-01-30 13:00:00  684.0
+1970-01-30 14:00:00  684.0
+1970-01-30 15:00:00  684.0
+1970-01-30 16:00:00  684.0
+1970-01-30 17:00:00  684.0
+1970-01-30 18:00:00  684.0
+1970-01-30 19:00:00  684.0
+1970-01-30 20:00:00  684.0
+1970-01-30 21:00:00  684.0
+1970-01-30 22:00:00  684.0
+1970-01-30 23:00:00  684.0
+1970-01-31 00:00:00  684.0
+1970-01-31 01:00:00  684.0
+1970-01-31 02:00:00  684.0
+1970-01-31 03:00:00  684.0
+1970-01-31 04:00:00  684.0
+1970-01-31 05:00:00  684.0
+1970-01-31 06:00:00  684.0
+1970-01-31 07:00:00  684.0
+1970-01-31 08:00:00  684.0
+1970-01-31 09:00:00  684.0
+1970-01-31 10:00:00  684.0
+1970-01-31 11:00:00  684.0
+1970-01-31 12:00:00  684.0
+1970-01-31 13:00:00  684.0
+1970-01-31 14:00:00  684.0
+1970-01-31 15:00:00  684.0
+1970-01-31 16:00:00  684.0
+1970-01-31 17:00:00  684.0
+1970-01-31 18:00:00  684.0
+1970-01-31 19:00:00  684.0
+1970-01-31 20:00:00  684.0
+1970-01-31 21:00:00  684.0
+1970-01-31 22:00:00  684.0
+1970-01-31 23:00:00  684.0
+1970-02-01 00:00:00  684.0
+1970-02-01 01:00:00  684.0
+1970-02-01 02:00:00  684.0
+1970-02-01 03:00:00  684.0
+
+Error
+None
+
+
+
+
+

Visualization

+
+
[7]:
+
+
+
# Qualitatively, we can see what the forecaster is doing by plotting
+print("Forecast w/ ground truth time series")
+fig, ax = model.plot_forecast(time_series=test_data,
+                              time_series_prev=train_data,
+                              plot_time_series_prev=True)
+plt.show()
+
+print()
+print("Forecast without ground truth time series")
+fig, ax = model.plot_forecast(time_stamps=test_data.to_pd().index,
+                              time_series_prev=train_data,
+                              plot_time_series_prev=True)
+
+
+
+
+
+
+
+
+Forecast w/ ground truth time series
+
+
+
+
+
+
+../../_images/tutorials_forecast_4_ForecastNewModel_12_1.png +
+
+
+
+
+
+
+
+Forecast without ground truth time series
+
+
+
+
+
+
+../../_images/tutorials_forecast_4_ForecastNewModel_12_3.png +
+
+
+
+

Quantitative Evaluation

+

You may quantitatively evaluate your model as well. Here, we compute the sMAPE (symmetric Mean Average Percent Error) of the model’s forecast vs. the true data. For ground truth \(y \in \mathbb{R}^T\) and prediction \(\hat{y} \in \mathbb{R}^T\), the sMAPE is computed as

+
+\[\mathrm{sMAPE}(y, \hat{y}) = \frac{200}{T} \sum_{t = 1}^{T} \frac{\lvert \hat{y}_t - y_t \rvert}{\lvert\hat{y}_t\rvert + \lvert y_t \rvert}\]
+
+
[8]:
+
+
+
from merlion.evaluate.forecast import ForecastMetric
+smape = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=forecast)
+print(f"sMAPE = {smape:.3f}")
+
+
+
+
+
+
+
+
+sMAPE = 20.166
+
+
+
+
+

Defining a Forecaster-Based Anomaly Detector

+

It is quite straightforward to adapt a forecasting model into an anomaly detection model. You just need to create a new file in the appropriate directory and define class stubs with some basic headers. Multiple inheritance with ForecastingDetectorBase takes care of most of the heavy lifting.

+

The anomaly score returned by any forecasting-based anomaly detector is based on the residual between the predicted and true time series values.

+
+
[9]:
+
+
+
from merlion.evaluate.anomaly import TSADMetric
+from merlion.models.anomaly.forecast_based.base import ForecastingDetectorBase
+from merlion.models.anomaly.base import DetectorConfig
+from merlion.post_process.threshold import AggregateAlarms
+from merlion.transform.normalize import MeanVarNormalize
+
+
+# Define a config class which inherits from RepeatRecentConfig and DetectorConfig, in that order
+class RepeatRecentDetectorConfig(RepeatRecentConfig, DetectorConfig):
+    # Set a default anomaly score post-processing rule
+    _default_post_rule = AggregateAlarms(alm_threshold=3.0)
+
+    # The default data pre-processing transform is mean-variance normalization,
+    # so that anomaly scores are roughly aligned with z-scores
+    _default_transform = MeanVarNormalize()
+
+# Define a model class which inherits from ForecastingDetectorBase and RepeatRecent
+# in that order
+class RepeatRecentDetector(ForecastingDetectorBase, RepeatRecent):
+    # All we need to do is set the config class
+    config_class = RepeatRecentDetectorConfig
+
+
+
+
+
[10]:
+
+
+
# Train the anomaly detection variant
+model2 = RepeatRecentDetector(RepeatRecentDetectorConfig())
+model2.train(train_data)
+
+
+
+
+
[10]:
+
+
+
+
+                     anom_score
+1970-01-01 00:00:00   -0.212986
+1970-01-01 01:00:00   -0.120839
+1970-01-01 02:00:00    0.000000
+1970-01-01 03:00:00   -0.171719
+1970-01-01 04:00:00   -0.305278
+...                         ...
+1970-01-29 23:00:00   -0.190799
+1970-01-30 00:00:00   -0.038160
+1970-01-30 01:00:00   -0.203519
+1970-01-30 02:00:00   -0.082679
+1970-01-30 03:00:00   -0.349798
+
+[700 rows x 1 columns]
+
+
+
+
[11]:
+
+
+
# Obtain the anomaly detection variant's predictions on the test data
+model2.get_anomaly_score(test_data)
+
+
+
+
+
[11]:
+
+
+
+
+                     anom_score
+1970-01-30 04:00:00   -0.413397
+1970-01-30 05:00:00   -0.756835
+1970-01-30 06:00:00   -0.966714
+1970-01-30 07:00:00   -1.202032
+1970-01-30 08:00:00   -1.291072
+1970-01-30 09:00:00   -1.380111
+1970-01-30 10:00:00   -1.341952
+1970-01-30 11:00:00   -1.246552
+1970-01-30 12:00:00   -1.163873
+1970-01-30 13:00:00   -0.953994
+1970-01-30 14:00:00   -0.686876
+1970-01-30 15:00:00   -0.286198
+1970-01-30 16:00:00    0.178079
+1970-01-30 17:00:00    0.559676
+1970-01-30 18:00:00    0.928554
+1970-01-30 19:00:00    1.246552
+1970-01-30 20:00:00    1.329232
+1970-01-30 21:00:00    1.348311
+1970-01-30 22:00:00    1.316512
+1970-01-30 23:00:00    1.081193
+1970-01-31 00:00:00    0.756835
+1970-01-31 01:00:00    0.540597
+1970-01-31 02:00:00    0.426117
+1970-01-31 03:00:00    0.108119
+1970-01-31 04:00:00   -0.311638
+1970-01-31 05:00:00   -0.712316
+1970-01-31 06:00:00   -0.966714
+1970-01-31 07:00:00   -1.214752
+1970-01-31 08:00:00   -1.316512
+1970-01-31 09:00:00   -1.373751
+1970-01-31 10:00:00   -1.399191
+1970-01-31 11:00:00   -1.316512
+1970-01-31 12:00:00   -1.221112
+1970-01-31 13:00:00   -1.049393
+1970-01-31 14:00:00   -0.737755
+1970-01-31 15:00:00   -0.381598
+1970-01-31 16:00:00    0.076320
+1970-01-31 17:00:00    0.489717
+1970-01-31 18:00:00    0.814075
+1970-01-31 19:00:00    0.966714
+1970-01-31 20:00:00    0.979434
+1970-01-31 21:00:00    0.922194
+1970-01-31 22:00:00    0.782275
+1970-01-31 23:00:00    0.642356
+1970-02-01 00:00:00    0.457917
+1970-02-01 01:00:00    0.222599
+1970-02-01 02:00:00    0.120839
+1970-02-01 03:00:00   -0.158999
+
+
+
+
[12]:
+
+
+
# Visualize the anomaly detection variant's performance, with filtered anomaly scores
+fig, ax = model2.plot_anomaly(test_data, time_series_prev=train_data,
+                              filter_scores=True, plot_time_series_prev=False,
+                              plot_forecast=True)
+
+
+
+
+
+
+
+../../_images/tutorials_forecast_4_ForecastNewModel_19_0.png +
+
+
+
+ + +
+
+ +
+
+
+
+ + +
+ + Versions + v2.0.2 + + +
+ +
+
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+ + + +
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+ + + + + \ No newline at end of file diff --git a/v2.0.2/tutorials/forecast/4_ForecastNewModel.ipynb b/v2.0.2/tutorials/forecast/4_ForecastNewModel.ipynb new file mode 100644 index 000000000..79c6f7c1c --- /dev/null +++ b/v2.0.2/tutorials/forecast/4_ForecastNewModel.ipynb @@ -0,0 +1,585 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adding a New Forecasting Model\n", + "\n", + "This notebook provides a minimal example on how to add a new forecasting model to Merlion. We follow the instructions in [CONTRIBUTING.md](https://github.com/salesforce/Merlion/blob/main/CONTRIBUTING.md). We suggest you review this [notebook](1_ForecastFeatures.ipynb) explaining how to use a Merlion forecasting model before reading this one.\n", + "\n", + "More specifically, let's implement a forecasting model whose forecast is just equal to the most recent observed value of the time series metric. For a more complete example, see our implementation of `Sarima` [here](https://github.com/salesforce/Merlion/blob/main/merlion/models/forecast/sarima.py)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Config Class\n", + "\n", + "The first step of creating a new model is defining an appropriate config class, which inherits from `ForecasterConfig`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from merlion.models.forecast.base import ForecasterConfig\n", + "\n", + "class RepeatRecentConfig(ForecasterConfig):\n", + " def __init__(self, max_forecast_steps=None, **kwargs):\n", + " super().__init__(max_forecast_steps=max_forecast_steps, **kwargs)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Class\n", + "\n", + "Next we define the model itself, which must inherit from the `ForecasterBase` base class and define all abstract methods. See the API docs for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import OrderedDict\n", + "from typing import List, Tuple\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from merlion.models.forecast.base import ForecasterBase\n", + "from merlion.utils.time_series import to_pd_datetime\n", + "\n", + "\n", + "class RepeatRecent(ForecasterBase):\n", + " # The config class for RepeatRecent is RepeatRecentConfig, defined above\n", + " config_class = RepeatRecentConfig\n", + " \n", + " @property\n", + " def require_even_sampling(self):\n", + " \"\"\"\n", + " Many forecasters assume the input time series is sampled evenly.\n", + " That isn't a necessary assumption for this model, so override the property.\n", + " \"\"\"\n", + " return False\n", + " \n", + " def __init__(self, config):\n", + " \"\"\"\n", + " Sets the model config and any other local variables. Here, we initialize\n", + " the most_recent_value to None.\n", + " \"\"\"\n", + " super().__init__(config)\n", + " self.most_recent_value = None\n", + " \n", + " \n", + " def _train(self, train_data: pd.DataFrame, train_config=None) -> Tuple[pd.DataFrame, None]: \n", + " # \"Train\" the model. Here, we just gather the most recent values for each univariate.\n", + " self.most_recent_value = [(k, v.values[-1]) for k, v in train_data.items()]\n", + " \n", + " # The model's \"prediction\" for the training data, is just the value from one step before.\n", + " pred = np.concatenate((np.zeros((1, self.dim)), train_data.values[:-1]))\n", + " train_forecast = pd.DataFrame(pred, index=train_data.index, columns=train_data.columns)\n", + " \n", + " # This model doesn't have any notion of error\n", + " train_stderr = None\n", + " \n", + " # Return the train prediction & standard error\n", + " return train_forecast, train_stderr\n", + " \n", + " def _forecast(self, time_stamps: List[int],\n", + " time_series_prev: pd.DataFrame = None,\n", + " return_prev=False\n", + " ) -> Tuple[pd.DataFrame, None]:\n", + "\n", + " # Use time_series_prev's most recent value if time_series_prev is given.\n", + " # Otherwise, use the most recent value stored from the training data\n", + " if time_series_prev is not None:\n", + " most_recent_value = [(k, v.values[-1]) for k, v in time_series_prev.items()]\n", + " else:\n", + " most_recent_value = self.most_recent_value\n", + " \n", + " # The forecast is just the most recent value repeated for every upcoming timestamp.\n", + " # Note that we only care about the target_seq_index here.\n", + " i = self.target_seq_index\n", + " datetimes = to_pd_datetime(time_stamps)\n", + " name, val = most_recent_value[i]\n", + " forecast = pd.DataFrame([val] * len(datetimes), index=datetimes, columns=[name])\n", + " \n", + " # If desired, pre-pend \"predicted\" vals of the target_seq_index of time_series_prev.\n", + " if return_prev and time_series_prev is not None:\n", + " pred = np.concatenate(([0], time_series_prev.values[:-1, i]))\n", + " prev_forecast = pd.DataFrame(pred, index=time_series_prev.index, columns=[name])\n", + " forecast = pd.concat((prev_forecast, forecast))\n", + "\n", + " return forecast, None\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the Model: A Simple Example\n", + "\n", + "Let's try running this model on some actual data! This next part assumes you've installed `ts_datasets`. We'll begin by getting a time series from the M4 dataset & visualizing it." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "from merlion.utils import TimeSeries, UnivariateTimeSeries\n", + "from ts_datasets.forecast import M4\n", + "\n", + "time_series, metadata = M4(subset=\"Hourly\")[0]\n", + "\n", + "# Visualize the full time series\n", + "fig = plt.figure(figsize=(12, 6))\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(time_series)\n", + "\n", + "# Label the train/test split with a dashed line\n", + "ax.axvline(time_series[metadata[\"trainval\"]].index[-1], ls=\"--\", lw=2, c=\"k\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we'll split the data into train & test splits, and run our forecasting model on it." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "train_data = TimeSeries.from_pd(time_series[metadata[\"trainval\"]])\n", + "test_data = TimeSeries.from_pd(time_series[~metadata[\"trainval\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "( H1\n", + " 1970-01-01 00:00:00 0.0\n", + " 1970-01-01 01:00:00 605.0\n", + " 1970-01-01 02:00:00 586.0\n", + " 1970-01-01 03:00:00 586.0\n", + " 1970-01-01 04:00:00 559.0\n", + " ... ...\n", + " 1970-01-29 23:00:00 820.0\n", + " 1970-01-30 00:00:00 790.0\n", + " 1970-01-30 01:00:00 784.0\n", + " 1970-01-30 02:00:00 752.0\n", + " 1970-01-30 03:00:00 739.0\n", + " \n", + " [700 rows x 1 columns],\n", + " None)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initialize a model & train it. The dataframe returned & printed\n", + "# below is the model's \"forecast\" on the training data. None is \n", + "# the uncertainty estimate.\n", + "model = RepeatRecent(RepeatRecentConfig())\n", + "model.train(train_data=train_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Forecast\n", + " H1\n", + "1970-01-30 04:00:00 684.0\n", + "1970-01-30 05:00:00 684.0\n", + "1970-01-30 06:00:00 684.0\n", + "1970-01-30 07:00:00 684.0\n", + "1970-01-30 08:00:00 684.0\n", + "1970-01-30 09:00:00 684.0\n", + "1970-01-30 10:00:00 684.0\n", + "1970-01-30 11:00:00 684.0\n", + "1970-01-30 12:00:00 684.0\n", + "1970-01-30 13:00:00 684.0\n", + "1970-01-30 14:00:00 684.0\n", + "1970-01-30 15:00:00 684.0\n", + "1970-01-30 16:00:00 684.0\n", + "1970-01-30 17:00:00 684.0\n", + "1970-01-30 18:00:00 684.0\n", + "1970-01-30 19:00:00 684.0\n", + "1970-01-30 20:00:00 684.0\n", + "1970-01-30 21:00:00 684.0\n", + "1970-01-30 22:00:00 684.0\n", + "1970-01-30 23:00:00 684.0\n", + "1970-01-31 00:00:00 684.0\n", + "1970-01-31 01:00:00 684.0\n", + "1970-01-31 02:00:00 684.0\n", + "1970-01-31 03:00:00 684.0\n", + "1970-01-31 04:00:00 684.0\n", + "1970-01-31 05:00:00 684.0\n", + "1970-01-31 06:00:00 684.0\n", + "1970-01-31 07:00:00 684.0\n", + "1970-01-31 08:00:00 684.0\n", + "1970-01-31 09:00:00 684.0\n", + "1970-01-31 10:00:00 684.0\n", + "1970-01-31 11:00:00 684.0\n", + "1970-01-31 12:00:00 684.0\n", + "1970-01-31 13:00:00 684.0\n", + "1970-01-31 14:00:00 684.0\n", + "1970-01-31 15:00:00 684.0\n", + "1970-01-31 16:00:00 684.0\n", + "1970-01-31 17:00:00 684.0\n", + "1970-01-31 18:00:00 684.0\n", + "1970-01-31 19:00:00 684.0\n", + "1970-01-31 20:00:00 684.0\n", + "1970-01-31 21:00:00 684.0\n", + "1970-01-31 22:00:00 684.0\n", + "1970-01-31 23:00:00 684.0\n", + "1970-02-01 00:00:00 684.0\n", + "1970-02-01 01:00:00 684.0\n", + "1970-02-01 02:00:00 684.0\n", + "1970-02-01 03:00:00 684.0\n", + "\n", + "Error\n", + "None\n" + ] + } + ], + "source": [ + "# Let's run our model on the test data now\n", + "forecast, err = model.forecast(test_data.to_pd().index)\n", + "print(\"Forecast\")\n", + "print(forecast)\n", + "print()\n", + "print(\"Error\")\n", + "print(err)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Forecast w/ ground truth time series\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Forecast without ground truth time series\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Qualitatively, we can see what the forecaster is doing by plotting\n", + "print(\"Forecast w/ ground truth time series\")\n", + "fig, ax = model.plot_forecast(time_series=test_data,\n", + " time_series_prev=train_data,\n", + " plot_time_series_prev=True)\n", + "plt.show()\n", + "\n", + "print()\n", + "print(\"Forecast without ground truth time series\")\n", + "fig, ax = model.plot_forecast(time_stamps=test_data.to_pd().index,\n", + " time_series_prev=train_data,\n", + " plot_time_series_prev=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quantitative Evaluation\n", + "\n", + "You may quantitatively evaluate your model as well. Here, we compute the sMAPE (symmetric Mean Average Percent Error) of the model's forecast vs. the true data. For ground truth $y \\in \\mathbb{R}^T$ and prediction $\\hat{y} \\in \\mathbb{R}^T$, the sMAPE is computed as\n", + "\n", + "$$\n", + "\\mathrm{sMAPE}(y, \\hat{y}) = \\frac{200}{T} \\sum_{t = 1}^{T} \\frac{\\lvert \\hat{y}_t - y_t \\rvert}{\\lvert\\hat{y}_t\\rvert + \\lvert y_t \\rvert}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sMAPE = 20.166\n" + ] + } + ], + "source": [ + "from merlion.evaluate.forecast import ForecastMetric\n", + "smape = ForecastMetric.sMAPE.value(ground_truth=test_data, predict=forecast)\n", + "print(f\"sMAPE = {smape:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining a Forecaster-Based Anomaly Detector\n", + "\n", + "It is quite straightforward to adapt a forecasting model into an anomaly detection model. You just need to create a new file in the appropriate [directory](https://github.com/salesforce/Merlion/blob/main/merlion/models/anomaly/forecast_based) and define class stubs with some basic headers. Multiple inheritance with `ForecastingDetectorBase` takes care of most of the heavy lifting.\n", + "\n", + "The anomaly score returned by any forecasting-based anomaly detector is based on the residual between the predicted and true time series values. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from merlion.evaluate.anomaly import TSADMetric\n", + "from merlion.models.anomaly.forecast_based.base import ForecastingDetectorBase\n", + "from merlion.models.anomaly.base import DetectorConfig\n", + "from merlion.post_process.threshold import AggregateAlarms\n", + "from merlion.transform.normalize import MeanVarNormalize\n", + "\n", + "\n", + "# Define a config class which inherits from RepeatRecentConfig and DetectorConfig, in that order\n", + "class RepeatRecentDetectorConfig(RepeatRecentConfig, DetectorConfig):\n", + " # Set a default anomaly score post-processing rule\n", + " _default_post_rule = AggregateAlarms(alm_threshold=3.0)\n", + " \n", + " # The default data pre-processing transform is mean-variance normalization,\n", + " # so that anomaly scores are roughly aligned with z-scores\n", + " _default_transform = MeanVarNormalize()\n", + "\n", + "# Define a model class which inherits from ForecastingDetectorBase and RepeatRecent\n", + "# in that order\n", + "class RepeatRecentDetector(ForecastingDetectorBase, RepeatRecent):\n", + " # All we need to do is set the config class\n", + " config_class = RepeatRecentDetectorConfig" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " anom_score\n", + "1970-01-01 00:00:00 -0.212986\n", + "1970-01-01 01:00:00 -0.120839\n", + "1970-01-01 02:00:00 0.000000\n", + "1970-01-01 03:00:00 -0.171719\n", + "1970-01-01 04:00:00 -0.305278\n", + "... ...\n", + "1970-01-29 23:00:00 -0.190799\n", + "1970-01-30 00:00:00 -0.038160\n", + "1970-01-30 01:00:00 -0.203519\n", + "1970-01-30 02:00:00 -0.082679\n", + "1970-01-30 03:00:00 -0.349798\n", + "\n", + "[700 rows x 1 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Train the anomaly detection variant\n", + "model2 = RepeatRecentDetector(RepeatRecentDetectorConfig())\n", + "model2.train(train_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " anom_score\n", + "1970-01-30 04:00:00 -0.413397\n", + "1970-01-30 05:00:00 -0.756835\n", + "1970-01-30 06:00:00 -0.966714\n", + "1970-01-30 07:00:00 -1.202032\n", + "1970-01-30 08:00:00 -1.291072\n", + "1970-01-30 09:00:00 -1.380111\n", + "1970-01-30 10:00:00 -1.341952\n", + "1970-01-30 11:00:00 -1.246552\n", + "1970-01-30 12:00:00 -1.163873\n", + "1970-01-30 13:00:00 -0.953994\n", + "1970-01-30 14:00:00 -0.686876\n", + "1970-01-30 15:00:00 -0.286198\n", + "1970-01-30 16:00:00 0.178079\n", + "1970-01-30 17:00:00 0.559676\n", + "1970-01-30 18:00:00 0.928554\n", + "1970-01-30 19:00:00 1.246552\n", + "1970-01-30 20:00:00 1.329232\n", + "1970-01-30 21:00:00 1.348311\n", + "1970-01-30 22:00:00 1.316512\n", + "1970-01-30 23:00:00 1.081193\n", + "1970-01-31 00:00:00 0.756835\n", + "1970-01-31 01:00:00 0.540597\n", + "1970-01-31 02:00:00 0.426117\n", + "1970-01-31 03:00:00 0.108119\n", + "1970-01-31 04:00:00 -0.311638\n", + "1970-01-31 05:00:00 -0.712316\n", + "1970-01-31 06:00:00 -0.966714\n", + "1970-01-31 07:00:00 -1.214752\n", + "1970-01-31 08:00:00 -1.316512\n", + "1970-01-31 09:00:00 -1.373751\n", + "1970-01-31 10:00:00 -1.399191\n", + "1970-01-31 11:00:00 -1.316512\n", + "1970-01-31 12:00:00 -1.221112\n", + "1970-01-31 13:00:00 -1.049393\n", + "1970-01-31 14:00:00 -0.737755\n", + "1970-01-31 15:00:00 -0.381598\n", + "1970-01-31 16:00:00 0.076320\n", + "1970-01-31 17:00:00 0.489717\n", + "1970-01-31 18:00:00 0.814075\n", + "1970-01-31 19:00:00 0.966714\n", + "1970-01-31 20:00:00 0.979434\n", + "1970-01-31 21:00:00 0.922194\n", + "1970-01-31 22:00:00 0.782275\n", + "1970-01-31 23:00:00 0.642356\n", + "1970-02-01 00:00:00 0.457917\n", + "1970-02-01 01:00:00 0.222599\n", + "1970-02-01 02:00:00 0.120839\n", + "1970-02-01 03:00:00 -0.158999" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Obtain the anomaly detection variant's predictions on the test data\n", + "model2.get_anomaly_score(test_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the anomaly detection variant's performance, with filtered anomaly scores\n", + "fig, ax = model2.plot_anomaly(test_data, time_series_prev=train_data,\n", + " filter_scores=True, plot_time_series_prev=False,\n", + " plot_forecast=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}