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

Author

Listed:
  • Arkadiusz Lipiecki
  • Kaja Bilinska
  • Nikolaos Kourentzes
  • Rafal Weron

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.

Suggested Citation

  • Arkadiusz Lipiecki & Kaja Bilinska & Nikolaos Kourentzes & Rafal Weron, 2025. "Stealing accuracy: Predicting day-ahead electricity prices with Temporal Hierarchy Forecasting (THieF)," WORking papers in Management Science (WORMS) WORMS/25/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    • Handle: RePEc:ahh:wpaper:worms2506
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    File URL: https://worms.pwr.edu.pl/RePEc/ahh/wpaper/WORMS_25_06.pdf
    File Function: Original version, 14.08.2025
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    References listed on IDEAS

    as
    1. Serafin, Tomasz & Weron, Rafał, 2025. "Loss functions in regression models: Impact on profits and risk in day-ahead electricity trading," Energy Economics, Elsevier, vol. 148(C).
    2. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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