ReModels: Quantile Regression Averaging models

TL;DR

This paper introduces a Python package for Quantile Regression Averaging (QRA) models, enhancing probabilistic electricity price forecasting by including recent modifications, data handling, and evaluation tools.

Contribution

It provides a comprehensive Python package implementing QRA and its recent variants, along with data processing and evaluation functionalities for electricity market predictions.

Findings

Includes recent modifications of QRA in the package

Facilitates data acquisition and preparation for electricity markets

Provides tools for evaluating probabilistic forecasts

Abstract

Electricity price forecasts play a crucial role in making key business decisions within the electricity markets. A focal point in this domain are probabilistic predictions, which delineate future price values in a more comprehensive manner than simple point forecasts. The golden standard in probabilistic approaches to predict energy prices is the Quantile Regression Averaging (QRA) method. In this paper, we present a Python package that encompasses the implementation of QRA, along with modifications of this approach that have appeared in the literature over the past few years. The proposed package also facilitates the acquisition and preparation of data related to electricity markets, as well as the evaluation of model predictions.