Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF)

Arkadiusz Lipiecki, Kaja Bilinska, Nicolaos Kourentzes and Rafał Weron

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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.

Date: 2025-08
New Economics Papers: this item is included in nep-cmp, nep-ene and nep-for
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Working Paper: Stealing accuracy: Predicting day-ahead electricity prices with Temporal Hierarchy Forecasting (THieF) (2025)
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