ROLCH: Regularized Online Learning for Conditional Heteroskedasticity

Introduction

This package provides online estimation of models for distributional regression respectively models for conditional heteroskedastic data. The main contribution is an online/incremental implementation of the generalized additive models for location, shape and scale (GAMLSS, see Rigby & Stasinopoulos, 2005) developed in Hirsch, Berrisch & Ziel, 2024.

Please have a look at the documentation or the example notebook.

We're actively working on the package and welcome contributions from the community. Have a look at the Release Notes and the Issue Tracker.

Install from PyPI

The package is available from pypi.

1) pip install rolch. 2) Enjoy

Install from source:

1) Clone this repo. 2) Install the necessary dependencies from the requirements.txt using conda create --name <env> --file requirements.txt. 3) Run python3 -m build to build the wheel. 4) Run pip install dist/rolch-0.1.0-py3-none-any.whl with the accurate version. If necessary, append --force-reinstall 5) Enjoy.

Authors

• Simon Hirsch, University of Duisburg-Essen & Statkraft
• Jonathan Berrisch, University of Duisburg-Essen
• Florian Ziel, University of Duisburg-Essen

Acknowledgements

Simon is employed at Statkraft and gratefully acknowledges support received from Statkraft for his PhD studies. This work contains the author's opinion and not necessarily reflects Statkraft's position.

Dependencies

ROLCH is designed to have minimal dependencies. We rely on python>=3.10, numpy, numba and scipy in a reasonably up-to-date versions.

Formater

We use ruff and black.