Regularized Quantile Regression Averaging for probabilistic electricity price forecasting

Bartosz Uniejewski and Rafał Weron

No HSC/19/04, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Science and Technology

Abstract: Quantile Regression Averaging (QRA) has sparked interest in the electricity price forecasting community after its unprecedented success in the Global Energy Forecasting Competition 2014, where the top two winning teams in the price track used variants of QRA. However, recent studies have reported the method's vulnerability to low quality predictors when the set of regressors is larger than just a few. To address this issue, we consider a regularized variant of QRA, which utilizes the Least Absolute Shrinkage and Selection Operator (LASSO) to automatically select the relevant regressors. We evaluate the introduced technique – dubbed LASSO QRA or LQRA for short – using datasets from the Polish and Nordic power markets, a set of 25 point forecasts obtained for calibration windows of different lengths and 20 different values of the regularization parameter. By comparing against nearly 30 benchmarks, we provide evidence for its superior predictive performance in terms of the Kupiec test, the pinball score and the test for conditional predictive accuracy.

Keywords: Electricity price forecasting; Probabilistic forecast; Quantile Regression Averaging; LASSO; Kupiec test; Pinball score; Conditional predictive accuracy (search for similar items in EconPapers)
JEL-codes: C22 C32 C51 C52 C53 Q41 Q47 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2019-11-16
New Economics Papers: this item is included in nep-ecm, nep-ene, nep-for and nep-ore
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Related works:
Journal Article: Regularized quantile regression averaging for probabilistic electricity price forecasting (2021)
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