Hierarchical Time Series forecasting
Project description
Hierarchical Time Series with a familiar API
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.
Note
STATUS: alpha. Active development, but breaking changes may come.
Features
Supported and tested on 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
Examples
You can find code usages here: https://github.com/carlomazzaferro/scikit-hts-examples
Roadmap
More flexible underlying modeling support
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
History
0.1.0 (2020-01-02)
First release on PyPI.
0.2.0 (2018-02-13)
Major feature implementation and documentation
Static typing
Testing - 44% coverage
0.2.3 (2020-03-28)
Testing up to 75%
Exogenous variable support
Extensive docs
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for scikit_hts-0.3.0-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2fd43b26a635e2316d1e892c9ccbad0b7b739f6700bd8630c71f7cdb673fbde2 |
|
MD5 | 1412a095d59d6a0a6760bd17d738bd3a |
|
BLAKE2b-256 | 9217070d854768be8461043acb1fa8987ffce9c3fb99023df5a34c37ecfa8b85 |