scikit-hts

Hierarchical Time Series with a familiar API

• MIT License
• Documentation: https://scikit-hts.readthedocs.io/en/latest/

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

• Supported and tested on python 3.6, python 3.7 and python 3.8
• 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
• Distributed training & Dask integration: perform training and prediction in parallel or in a cluster with Dask

Examples

You can find code usages here: https://github.com/carlomazzaferro/scikit-hts-examples

Roadmap

• More flexible underlying modeling support

  • [P] AR, ARIMAX, VARMAX, etc
  • [P] Bring-Your-Own-Model
  • [P] Different parameters for each of the models

• Decoupling reconciliation methods from forecast fitting

  • [W] Enable to use the reconciliation methods with pre-fitted models

P: Planned

W: WIP

Credits

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