Stealing accuracy: Predicting day-ahead electricity prices with Temporal Hierarchy Forecasting (THieF)

Arkadiusz Lipiecki, Kaja Bilinska, Nikolaos Kourentzes and Rafał Weron

No WORMS/25/06, WORking papers in Management Science (WORMS) from Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology

Abstract: We introduce the concept of Temporal Hierarchy Forecasting (THieF) in predicting day-ahead electricity prices and show that reconciling forecasts for hourly products, 2- to 12-hour blocks, and baseload contracts significantly (up to 13%) improves accuracy at all levels. These results remain consistent throughout a challenging 4-year test period (2021-2024) in the German power market and across model architectures, including linear regression, a shallow neural network, gradient boosting, and a state-of-the-art transformer. Given that (i) trading of block products is becoming more common and (ii) the computational cost of reconciliation is comparable to that of predicting hourly prices alone, we recommend using it in daily forecasting practice.

Keywords: Electricity price; Temporal Hierarchy Forecasting (THieF); Forecast reconciliation; Regression; Machine learning (search for similar items in EconPapers)
JEL-codes: C22 C45 C51 C53 Q41 Q47 (search for similar items in EconPapers)
Pages: 4 pages
Date: 2025
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene and nep-for
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https://worms.pwr.edu.pl/RePEc/ahh/wpaper/WORMS_25_06.pdf Original version, 14.08.2025 (application/pdf)

Related works:
Working Paper: Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF) (2025)
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