arXiv 3 Apr 2025 · Statistics — Machine Learning · 1 citations (OpenAlex)
arXiv:2504.02518 · PDF · DOI · OpenAlex · Extracted main text
Probabilistic electricity price forecasting (PEPF) is vital for short-term electricity markets, yet the multivariate nature of day-ahead prices - spanning 24 consecutive hours - remains underexplored. At the same time, real-time decision-making requires methods that are both accurate and fast. We introduce an online algorithm for multivariate distributional regression models, allowing an efficient modelling of the conditional means, variances, and dependence structures of electricity prices. The approach combines multivariate distributional regression with online coordinate descent and LASSO-type regularization, enabling scalable estimation in high-dimensional covariate spaces. Additionally, we propose a regularized estimation path over increasingly complex dependence structures, allowing for early stopping and avoiding overfitting. In a case study of the German day-ahead market, our method outperforms a wide range of benchmarks, showing that modeling dependence improves both calibration and predictive accuracy. Furthermore, we analyse the trade-off between predictive accuracy and computational costs for batch and online estimation and provide an high-performing open-source Python implementation in the ondil package.
appendix boundary found by appendix_command · 88% of the source is main text. Read the extracted text to check this.
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 | Muschinski, Thomas and Mayr, Georg J and Simon, Thorsten and Umlauf,… (2022) Cholesky-based multivariate Gaussian regression | 1.000 | 8 | 3 | 100% |
| 2 | Marcjasz, Grzegorz and Narajewski, Michał and Weron, Rafał and Ziel,… (2023) Distributional neural networks for electricity price forecasting | 1.000 | 7 | 5 | 100% |
| 3 | Lipiecki, Arkadiusz and Uniejewski, Bartosz and Weron, Rafał (2024) Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotoni… | 1.000 | 6 | 3 | 100% |
| 4 | Kock, Lucas and Klein, Nadja (2025) Truly multivariate structured additive distributional regression | 1.000 | 5 | 3 | 100% |
| 5 | Hirsch, Simon and Berrisch, Jonathan and Ziel, Florian (2024) Online Distributional Regression self | 0.961 | 18 | 6 | 89% |
| 6 | Lago, Jesus and Marcjasz, Grzegorz and De Schutter, Bart and Weron,… (2021) Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark | 0.928 | 5 | 3 | 80% |
| 7 | Gioia, Vincenzo and Fasiolo, Matteo and Browell, Jethro and Bellio,… (2025) Additive covariance matrix models: modeling regional electricity net-demand in Great Britain | 0.928 | 4 | 3 | 100% |
| 8 | Muniain, Peru and Ziel, Florian (2020) Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices | 0.843 | 3 | 3 | 100% |
| 9 | Marcotte, Étienne and Zantedeschi, Valentina and Drouin, Alexandre a… (2023) Regions of reliability in the evaluation of multivariate probabilistic forecasts | 0.811 | 4 | 2 | 100% |
| 10 | Nowotarski, Jakub and Weron, Rafał (2018) Recent advances in electricity price forecasting: A review of probabilistic forecasting | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 90 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Probabilistic Forecasting for Day-ahead Electricity Prices, Battery Trading Strategies and the Economic Evaluation of Predictive Accuracy | 0.644 | 3 | 2 |
| 2 | Online Distributional Regression | 0.405 | 1 | 1 |