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Hierarchical Time Series forecasting

Project description

Hierarchical Time Series with a familiar API

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Overview

Building on the excellent work by Hyndman [1], we developed this package in order to provide a python implementation of general hierarchical time series modeling.

[1]Forecasting Principles and Practice. Rob J Hyndman and George Athanasopoulos. Monash University, Australia.

Note

STATUS: alpha. Active development, but breaking changes may come.

Features

  • Implementation of Bottom-Up, Top-Down, Middle-Out, Forecast Proportions, Average Historic Proportions, Proportions of Historic Averages and OLS revision methods
  • Support for a variety of underlying forecasting models, inlcuding: SARIMAX, ARIMA, Prophet, Holt-Winters
  • Scikit-learn-like API
  • Geo events handling functionality for geospatial data, including visualisation capabilities
  • Static typing for a nice developer experience

Roadmap

  • More flexible underlying modeling support

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

Project details


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