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

Arkadiusz Lipiecki; Kaja Bilinska; Nicolaos Kourentzes; Rafal Weron

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arXiv · 2508.11372

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

Arkadiusz Lipiecki·Kaja Bilinska·Nicolaos Kourentzes·Rafal 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 and 2- to 24-hour blocks can significantly (up to 13%) improve accuracy at all levels. These results remain consistent throughout a challenging 4-year test period (2021-2024) in the German and Spanish power markets and across model architectures, including linear regression, shallow feedforward neural networks, gradient-boosted decision trees, and a state-of-the-art, pretrained 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.

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Arkadiusz Lipiecki, Kaja Bilinska, Nicolaos Kourentzes, Rafal Weron. 2025-08-15. Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF). https://arxiv.org/abs/2508.11372

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