Computing electricity spot price prediction intervals using quantile regression and forecast averaging

Jakub Nowotarski and Rafał Weron

Computational Statistics, 2015, vol. 30, issue 3, 803 pages

Abstract: We examine possible accuracy gains from forecast averaging in the context of interval forecasts of electricity spot prices. First, we test whether constructing empirical prediction intervals (PI) from combined electricity spot price forecasts leads to better forecasts than those obtained from individual methods. Next, we propose a new method for constructing PI—Quantile Regression Averaging (QRA)—which utilizes the concept of quantile regression and a pool of point forecasts of individual (i.e. not combined) models. While the empirical PI from combined forecasts do not provide significant gains, the QRA-based PI are found to be more accurate than those of the best individual model—the smoothed nonparametric autoregressive model. Copyright The Author(s) 2015

Keywords: Quantile regression averaging; Prediction interval; Quantile regression; Forecasts combination; Electricity spot price (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (65)

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Working Paper: Computing electricity spot price prediction intervals using quantile regression and forecast averaging (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:30:y:2015:i:3:p:791-803

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DOI: 10.1007/s00180-014-0523-0

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