Metadata-Version: 2.1
Name: scikit-hts
Version: 0.2.1
Summary: Hierarchical Time Series forecasting
Home-page: UNKNOWN
Author: Carlo Mazzaferro
Author-email: carlo.mazzaferro@gmail.com
License: UNKNOWN
Keywords: scikit-hts
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Dist: numpy (>=1.17.4)
Requires-Dist: pandas (>=0.25.3)
Requires-Dist: scipy (>=1.3.2)
Requires-Dist: scikit-learn (>=0.21.3)
Requires-Dist: pmdarima (==1.5.1)
Requires-Dist: statsmodels (==0.10.2)
Requires-Dist: fbprophet (>=0.5)
Requires-Dist: holidays (==0.9.12)

##########
scikit-hts
##########

Hierarchical Time Series with a familiar API


.. image:: https://travis-ci.org/carlomazzaferro/scikit-hts.svg?branch=master
    :target: https://travis-ci.org/carlomazzaferro/scikit-hts

.. 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://coveralls.io/repos/github/carlomazzaferro/scikit-hts/badge.svg?branch=master
    :target: https://coveralls.io/github/carlomazzaferro/scikit-hts?branch=master
    :alt: Coverage

.. image:: https://pepy.tech/badge/scikit-hts/month
     :target: https://pepy.tech/project/scikit-hts/month
     :alt: Downloads/Month




* `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 <https://otexts.com/fpp2/>`_.

.. 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.

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



=======
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



