GEFCOM 2014 - Probabilistic Electricity Price Forecasting

TL;DR

This paper compares ARMA with exogenous variables and Gradient Boosting Regression for probabilistic electricity price forecasting, demonstrating that a multi-model approach improves accuracy on real-world data.

Contribution

It introduces a comparative analysis of traditional ARMA and modern Gradient Boosting methods for energy price forecasting using real-world data from a major competition.

Findings

Gradient Boosting achieves lower normalized mean absolute error.

Multi-model approach outperforms individual models.

Gradient Boosting handles seasonality and auto-correlation effectively.

Abstract

Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a well-known and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting Competition 2014 with a year long rolling window forecast. The results from the experiment reveal that a multi-model approach is significantly better performing in terms of error metrics. Gradient Boosting can deal with seasonality and auto-correlation out-of-the box and achieve lower rate of normalized mean absolute error on real-world data.