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
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-0.2.3-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2b646cad5d7d128780ce538cf98c1e61fc892a4eb62811953d4b08fd38ca93da |
|
MD5 | 5f5bf9a760eb83aa6ebc0690bd7e3620 |
|
BLAKE2b-256 | 47f18b2764ddcf011cfdbdb0ad9110ac534b8091388aa8df5b4b6bfab025b651 |