Variance stabilizing transformations for electricity spot price forecasting

Bartosz Uniejewski, Rafał Weron and Florian Ziel

No HSC/17/01, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Science and Technology

Abstract: Most electricity spot price series exhibit price spikes. These extreme observations may significantly impact the obtained model estimates and hence reduce efficiency of the employed predictive algorithms. For markets with only positive prices the logarithmic transform is the single most commonly used technique to reduce spike severity and consequently stabilize the variance. However, for datasets with very close to zero (like the Spanish) or negative (like the German) prices the log-transform is not feasible. What reasonable choices do we have then? To address this issue, we conduct a comprehensive forecasting study involving 12 datasets from diverse power markets and evaluate 16 variance stabilizing transformations. We find that the probability integral transform (PIT) combined with the standard Gaussian distribution yields the best approach, significantly better than many of the considered alternatives.

Keywords: Electricity spot price; Forecasting; Variance stabilizing transformation; Probability integral transform; Price spike; Diebold-Mariano test (search for similar items in EconPapers)
JEL-codes: C14 C22 C51 C53 Q47 (search for similar items in EconPapers)
Pages: 10 pages
Date: 2017-02-14
New Economics Papers: this item is included in nep-ene and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (39)

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