Auto-TS-Time-Series

AutoTS Forecasting

Overview

The AutoTS Forecasting project is a Python implementation leveraging the autots library for streamlined time series forecasting. This tool automates the process of generating accurate forecasts with minimal coding effort. The primary focus is on predicting future values in time series data to facilitate well-informed decision-making.

Key Features

• Automatic Time Series Forecasting: Utilize the autots library to automate the generation of time series forecasts.

• Forecast Visualization: Visualize forecast results for different categories, providing a clear understanding of predicted trends.

• Customizable Forecasting Model: The code includes the creation of a forecasting model with various customizable parameters, allowing users to tailor the model to their specific needs.

Usage

1. Import Required Libraries:

   from autots import AutoTS
   import pandas as pd
   import numpy as np

2. Load Time Series Data:

   # Example: Loading data from a CSV file
   df_all = pd.read_csv(r"Path of temp data.xlsx")
   df_all['Date'] = pd.to_datetime(df_all['Date'], format="%d-%m-%Y")

3. Create Forecasting Model:

   model = AutoTS(
       forecast_length=15,
       frequency='infer',
       prediction_interval=0.9,
       ensemble='auto',
       model_list="fast_parallel",  # Options: "superfast", "default", "fast_parallel"
       transformer_list="fast",  # Options: "superfast",
       drop_most_recent=1,
       max_generations=10,
       num_validations=4,
       validation_method="backwards"
   )
   
   model = model.fit(
       df_all[df_all.Date < dt],
       date_col='Date' if long else None,
       value_col='Value' if long else None,
       id_col='Category' if long else None,
   )

4. Generate Predictions:

   prediction = model.predict()

5. Access Forecast Values:

   def getfcast_value(cat, d):
       try:
           ret_val = round(prediction.forecast.loc[d.strftime("%Y-%m-%d")][cat], 3)
           return ret_val
       except Exception as e:
           print(e)
           return np.NaN
   
   df_all['f1'] = df_all.apply(lambda x: getfcast_value(x['Category'], x['Date']) if x['Date'] >= dt else np.NaN, axis=1)

6. Visualize Forecast Results:

   for cat in df_all.Category.unique():
       df_all[(df_all.Category == cat) & (df_all.Date > '2022-01-01')].set_index('Date').drop('Category', axis=1).plot(
           title="Category : " + cat, figsize=(12, 3)
       )

Getting Started

1. Clone the repository:

   git clone https://github.com/your-username/your-repo.git
   

2. Install the required dependencies:

   pip install autots pandas numpy

3. Run the script