Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging
Katarzyna Maciejowska (),
Jakub Nowotarski and
Rafał Weron ()
No HSC/14/09, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Technology
We examine possible accuracy gains from using factor models, quantile regression and forecast averaging for computing interval forecasts of electricity spot prices. We extend the Quantile Regression Averaging (QRA) approach of Nowotarski and Weron (2014) and use principal component analysis to automate the selection process from among a large set of individual forecasting models available for averaging. We show that the resulting Factor Quantile Regression Averaging (FQRA) approach performs very well for price (and load) data from the British power market. In terms of unconditional coverage, conditional coverage and the Winkler score, we find the FQRA-implied prediction intervals to be more accurate than those of the benchmark ARX model and the QRA approach.
Keywords: Probabilistic forecasting; Prediction interval; Quantile regression; Factor model; Forecasts combination; Electricity spot price (search for similar items in EconPapers)
JEL-codes: C22 C32 C38 C53 Q47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ene and nep-for
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Forthcoming in International Journal of Forecasting (doi: 10.1016/j.ijforecast.2014.12.004), 2016.
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Journal Article: Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging (2016)
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