AutoTS

autots is an automated time series forecasting package for Python.

Features:

  • Finds optimal time series forecasting model and data transformations by genetic programming optimization

  • Handles univariate and multivariate/parallel time series

  • Point and probabilistic upper/lower bound forecasts for all models

  • Over twenty available model classes, with tens of thousands of possible hyperparameter configurations
    • Includes naive, statistical, machine learning, and deep learning models

    • Multiprocessing for univariate models for scalability on multivariate datasets

    • Ability to add external regressors

  • Over thirty time series specific data transformations
    • Ability to handle messy data by learning optimal NaN imputation and outlier removal

  • Allows automatic ensembling of best models
    • ‘horizontal’ ensembling on multivariate series - learning the best model for each series

  • Multiple cross validation options
    • ‘seasonal’ validation allows forecasts to be optimized for the season of your forecast period

  • Subsetting and weighting to improve speed and relevance of search on large datasets
    • ‘constraint’ parameter can be used to assure forecasts don’t drift beyond historic boundaries

  • Option to use one or a combination of metrics for model selection

  • Import and export of model templates for deployment and greater user customization

Installation

pip install autots

Requirements: Python 3.6+, numpy, pandas, statsmodels, and scikit-learn.

Modules API

Indices and tables