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.
@article{arxiv.1506.06972,
title = {GEFCOM 2014 - Probabilistic Electricity Price Forecasting},
author = {Gergo Barta and Gyula Borbely and Gabor Nagy and Sandor Kazi and Tamas Henk},
journal= {arXiv preprint arXiv:1506.06972},
year = {2015}
}
Comments
10 pages, 5 figures, KES-IDT 2015 conference. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19857-6_7