https://github.com/carlomazzaferro/scikit-hts

Hierarchical Time Series Forecasting with a familiar API
https://github.com/carlomazzaferro/scikit-hts

exponential-smoothing fbprophet hierarchical-data machine-learning scikit-learn statsmodels time-series time-series-analysis

Last synced: 2 days ago
JSON representation

Hierarchical Time Series Forecasting with a familiar API

• Host: GitHub
• URL: https://github.com/carlomazzaferro/scikit-hts
• Owner: carlomazzaferro
• License: mit
• Created: 2019-11-23T11:08:32.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-05-12T07:26:09.000Z (over 1 year ago)
• Last Synced: 2024-10-30T08:15:44.440Z (about 1 month ago)
• Topics: exponential-smoothing, fbprophet, hierarchical-data, machine-learning, scikit-learn, statsmodels, time-series, time-series-analysis
• Language: Python
• Homepage:
• Size: 266 KB
• Stars: 222
• Watchers: 17
• Forks: 39
• Open Issues: 23
• Metadata Files:
  • Readme: README.rst
  • Changelog: HISTORY.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE

Awesome Lists containing this project

• awesome-time-series - scikit-hts

README

        
NOTE: I unfortunately do not have time anymore to dedicate to this project, contributions are welcome.

##########

scikit-hts

##########

Hierarchical Time Series with a familiar API. This is the result from not having found any good implementations

of HTS on-line, and my work in the mobility space while working at Circ (acquired by Bird scooters).

My work on this is purely out of passion, so contributions are always

welcomed. You can also buy me a coffee if you'd like:

     ETH / BSC Address:  ``0xbF42b9c8F7B69D52b8b986AA4E0BAc6838Af6698``

.. image:: https://github.com/carlomazzaferro/scikit-hts/workflows/main%20workflow/badge.svg?branch=master

    :target: https://github.com/carlomazzaferro/scikit-hts/actions

.. image:: https://badge.fury.io/py/scikit-hts.svg

    :target: https://badge.fury.io/py/scikit-hts

.. image:: https://readthedocs.org/projects/racket/badge/?version=latest

    :target: https://racket.readthedocs.io/en/latest/?badge=latest

    :alt: Documentation Status

.. image:: https://codecov.io/gh/carlomazzaferro/scikit-hts/branch/master/graph/badge.svg?token=K4OAC8C51T

    :target: https://codecov.io/gh/carlomazzaferro/scikit-hts

    :alt: Coverage

.. image:: https://pepy.tech/badge/scikit-hts/month

     :target: https://pepy.tech/project/scikit-hts/month

     :alt: Downloads/Month

.. image:: https://img.shields.io/badge/join-us%20on%20slack-gray.svg?longCache=true&logo=slack&colorB=brightgreen

    :target: https://join.slack.com/t/scikit-hts/shared_invite/zt-d5is54bp-iOeagm7Jv68ZTkjk_zezrA

    :alt: Slack

* `MIT License`_

* Documentation: https://scikit-hts.readthedocs.io/en/latest/

.. _`MIT License`: https://github.com/carlomazzaferro/scikit-hts/blob/master/LICENSE

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 representations of hierarchical and grouped time series

* 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.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter

.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage