Arkadiusz Lipiecki, Kaja Bilinska, Nicolaos Kourentzes, Rafal Weron
arXiv 15 Aug 2025 · Finance — Statistical Finance · publishedInternational Journal of Forecasting (2026) · 1 citations (OpenAlex)
arXiv:2508.11372 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Tomasz Serafin and Rafał Weron (2025) Loss functions in regression models: Impact on profits and risk in day-ahead electricity trading self | 1.000 | 5 | 3 | 100% |
| 2 | F. Ziel and R. Weron (2018) Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks self | 0.941 | 6 | 4 | 83% |
| 3 | George Athanasopoulos and Rob J. Hyndman and Nikolaos Kourentzes and… (2017) Forecasting with temporal hierarchies self | 0.928 | 10 | 3 | 80% |
| 4 | Jesus Lago and Grzegorz Marcjasz and Bart De Schutter and Rafał Weron (2021) Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark self | 0.928 | 5 | 4 | 80% |
| 5 | Xiyuan Zhang and Danielle Maddix Robinson (2025) Mitra: Mixed synthetic priors for enhancing tabular foundation models | 0.737 | 3 | 2 | 100% |
| 6 | Ledoit, Olivier and Wolf, Michael (2004) Honey, I shrunk the sample covariance matrix | 0.644 | 3 | 2 | 67% |
| 7 | European Commission (2025) EU electricity trading in the day-ahead markets becomes more dynamic | 0.644 | 2 | 2 | 100% |
| 8 | Juliane Schäfer and Korbinian Strimmer (2005) A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics | 0.511 | 3 | 2 | 33% |
| 9 | K. Hubicka and G. Marcjasz and R. Weron (2019) A note on averaging day-ahead electricity price forecasts across calibration windows self | 0.511 | 2 | 1 | 100% |
| 10 | Nikolaos Kourentzes and Fotios Petropoulos and Juan R. Trapero (2014) Improving forecasting by estimating time series structural components across multiple frequencies self | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 39 scored citations.