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Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Parameter-rich models estimated via the LASSO

Arkadiusz Jędrzejewski (), Grzegorz Marcjasz () and Rafał Weron ()

No WORMS/21/04, WORking papers in Management Science (WORMS) from Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology

Abstract: Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based model is calibrated to the prices themselves. Here, we show that significant accuracy gains can be achieved also in the case of parameter-rich models estimated via the \textit{least absolute shrinkage and selection operator} (LASSO). Moreover, we provide insights as to the order of applying seasonal decomposition and variance stabilizing transformations before model calibration, and propose two well-performing forecast averaging schemes based on different approaches to modeling the long-term seasonal component.

Keywords: Electricity price forecasting; Day-ahead market; LASSO; Long-term seasonal component; Variance stabilizing transformation; Forecast averaging (search for similar items in EconPapers)
JEL-codes: C22 C32 C51 C53 Q41 Q47 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2021-03-28
New Economics Papers: this item is included in nep-ene, nep-for and nep-ore
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http://worms.pwr.edu.pl/RePEc/ahh/wpaper/WORMS_21_04.pdf Original version, 2021 (application/pdf)

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Journal Article: Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO (2021) Downloads
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