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

Jakub Nowotarski and Rafał Weron

No HSC/13/12, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Science and Technology

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, which utilizes the concept of quantile regression (QR) and a pool of point forecasts of individual (i.e. not combined) time series models. While the empirical PI from combined forecasts do not provide significant gains, the QR based PI are found to be more accurate than those of the best individual model - the smoothed nonparametric autoregressive model.

Keywords: Prediction interval; Quantile regression; Forecasts combination; Electricity spot price (search for similar items in EconPapers)
JEL-codes: C22 C24 C53 Q47 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2013-12-31
New Economics Papers: this item is included in nep-ecm, nep-ene and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_13_12.pdf Original version, 2013; Final version published in Computational Statistics (doi: 10.1007/s00180-014-0523-0) (application/pdf)

Related works:
Journal Article: Computing electricity spot price prediction intervals using quantile regression and forecast averaging (2015)
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