Automated Time Series Models in Python (AtsPy)

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Finance Quant Machine Learning

• ML-Quant.com - Automated Research Repository

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SSRN Report

Easily develop state of the art time series models to forecast univariate data series. Simply load your data and select which models you want to test. This is the largest repository of automated structural and machine learning time series models. Please get in contact if you want to contribute a model. This is a fledgling project, all advice appreciated.

Install

pip install atspy

Automated Models

1. ARIMA - Automated ARIMA Modelling
2. Prophet - Modeling Multiple Seasonality With Linear or Non-linear Growth
3. HWAAS - Exponential Smoothing With Additive Trend and Additive Seasonality
4. HWAMS - Exponential Smoothing with Additive Trend and Multiplicative Seasonality
5. NBEATS - Neural basis expansion analysis (now fixed at 20 Epochs)
6. Gluonts - RNN-based Model (now fixed at 20 Epochs)
7. TATS - Seasonal and Trend no Box Cox
8. TBAT - Trend and Box Cox
9. TBATS1 - Trend, Seasonal (one), and Box Cox
10. TBATP1 - TBATS1 but Seasonal Inference is Hardcoded by Periodicity
11. TBATS2 - TBATS1 With Two Seasonal Periods

Why AtsPy?

1. Implements all your favourite automated time series models in a unified manner by simply running AutomatedModel(df).
2. Reduce structural model errors with 30%-50% by using LightGBM with TSFresh infused features.
3. Automatically identify the seasonalities in your data using singular spectrum analysis, periodograms, and peak analysis.
4. Identifies and makes accessible the best model for your time series using in-sample validation methods.
5. Combines the predictions of all these models in a simple (average) and complex (GBM) ensembles for improved performance.
6. Where appropriate models have been developed to use GPU resources to speed up the automation process.
7. Easily access all the models by using am.models_dict_in for in-sample and am.models_dict_out for out-of-sample prediction.

AtsPy Progress

1. Univariate forecasting only (single column) and only monthly and daily data have been tested for suitability.
2. More work ahead; all suggestions and criticisms appreciated, use the issues tab.
3. Here is a Google Colab to run the package in the cloud and here you can run all the models.

Documentation by Example

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Load Package

from atspy import AutomatedModel

Pandas DataFrame

The data requires strict preprocessing, no periods can be skipped and there cannot be any empty values.

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/firmai/random-assets-two/master/ts/monthly-beer-australia.csv")
df.Month = pd.to_datetime(df.Month)
df = df.set_index("Month"); df

	Megaliters
Month
1956-01-01	93.2
1956-02-01	96.0
1956-03-01	95.2
1956-04-01	77.1
1956-05-01	70.9

AutomatedModel

1. AutomatedModel - Returns a class instance.
2. forecast_insample - Returns an in-sample forcasted dataframe and performance.
3. forecast_outsample - Returns an out-of-sample forcasted dataframe.
4. ensemble - Returns the results of three different forms of ensembles.
5. models_dict_in - Returns a dictionary of the fully trained in-sample models.
6. models_dict_out - Returns a dictionary of the fully trained out-of-sample models.

from atspy import AutomatedModel
model_list = ["HWAMS","HWAAS","TBAT"]
am = AutomatedModel(df = df , model_list=model_list,forecast_len=20 )

Other models to try, add as many as you like; note ARIMA is slow: ["ARIMA","Gluonts","Prophet","NBEATS", "TATS", "TBATS1", "TBATP1", "TBATS2"]

In-Sample Performance

forecast_in, performance = am.forecast_insample(); forecast_in

	Target	HWAMS	HWAAS	TBAT
Date
1985-10-01	181.6	161.962148	162.391653	148.410071
1985-11-01	182.0	174.688055	173.191756	147.999237
1985-12-01	190.0	189.728744	187.649575	147.589541
1986-01-01	161.2	155.077205	154.817215	147.180980
1986-02-01	155.5	148.054292	147.477692	146.773549

performance

	Target	HWAMS	HWAAS	TBAT
rmse	0.000000	17.599400	18.993827	36.538009
mse	0.000000	309.738878	360.765452	1335.026136
mean	155.293277	142.399639	140.577496	126.590412

Out-of-Sample Forecast

forecast_out = am.forecast_outsample(); forecast_out

	HWAMS	HWAAS	TBAT
Date
1995-09-01	137.518755	137.133938	142.906275
1995-10-01	164.136220	165.079612	142.865575
1995-11-01	178.671684	180.009560	142.827110
1995-12-01	184.175954	185.715043	142.790757
1996-01-01	147.166448	147.440026	142.756399

Ensemble and Model Validation Performance

all_ensemble_in, all_ensemble_out, all_performance = am.ensemble(forecast_in, forecast_out)

all_performance

	rmse	mse	mean
ensemble_lgb__X__HWAMS	9.697588	94.043213	146.719412
ensemble_lgb__X__HWAMS__X__HWAMS_HWAAS__X__ensemble_ts__X__HWAAS	9.875212	97.519817	145.250837
ensemble_lgb__X__HWAMS__X__HWAMS_HWAAS	11.127326	123.817378	142.994374
ensemble_lgb	12.748526	162.524907	156.487208
ensemble_lgb__X__HWAMS__X__HWAMS_HWAAS__X__ensemble_ts__X__HWAAS__X__HWAMS_HWAAS_TBAT__X__TBAT	14.589155	212.843442	138.615567
HWAMS	15.567905	242.359663	136.951615
HWAMS_HWAAS	16.651370	277.268110	135.544299
ensemble_ts	17.255107	297.738716	163.134079
HWAAS	17.804066	316.984751	134.136983
HWAMS_HWAAS_TBAT	23.358758	545.631579	128.785846
TBAT	39.003864	1521.301380	115.268940

Best Performing In-sample

all_ensemble_in[["Target","ensemble_lgb__X__HWAMS","HWAMS","HWAAS"]].plot()

Future Predictions All Models

all_ensemble_out[["ensemble_lgb__X__HWAMS","HWAMS","HWAAS"]].plot()

And Finally Grab the Models

am.models_dict_in

{'HWAAS': <statsmodels.tsa.holtwinters.HoltWintersResultsWrapper at 0x7f42f7822d30>,
 'HWAMS': <statsmodels.tsa.holtwinters.HoltWintersResultsWrapper at 0x7f42f77fff60>,
 'TBAT': <tbats.tbats.Model.Model at 0x7f42d3aab048>}

am.models_dict_out

{'HWAAS': <statsmodels.tsa.holtwinters.HoltWintersResultsWrapper at 0x7f9c01309278>,
 'HWAMS': <statsmodels.tsa.holtwinters.HoltWintersResultsWrapper at 0x7f9c01309cf8>,
 'TBAT': <tbats.tbats.Model.Model at 0x7f9c08f18ba8>}

Follow this link if you want to run the package in the cloud.

AtsPy Future Development

1. Additional in-sample validation steps to stop deep learning models from over and underfitting.
2. Extra performance metrics like MAPE and MAE.
3. Improved methods to select the window length to use in training and calibrating the model.
4. Add the ability to accept dirty data, and have the ability to clean it up, interpolation etc.
5. Add a function to resample to a larger frequency for big datasets.
6. Add the ability to algorithmically select a good enough chunk of a large dataset to balance performance and time to train.
7. More internal model optimisation using AIC, BIC an AICC.
8. Code annotations for other developers to follow and improve on the work being done.
9. Force seasonality stability between in and out of sample training models.
10. Make AtsPy less dependency heavy, currently it draws on tensorflow, pytorch and mxnet.

Citations

If you use AtsPy in your research, please consider citing it. I have also written a small report that can be found on SSRN.

BibTeX entry:

@software{atspy,
  title = {{AtsPy}: Automated Time Series Models in Python.},
  author = {Snow, Derek},
  url = {https://github.com/firmai/atspy/},
  version = {1.15},
  date = {2020-02-17},
}

@misc{atspy,
  author = {Snow, Derek},
  title = {{AtsPy}: Automated Time Series Models in Python (1.15).},
  year  = {2020},
  url   = {https://github.com/firmai/atspy/},
}