arXiv 29 Aug 2023 · Finance — Statistical Finance · publishedBadania Operacyjne i Decyzje/Operations Research and Decisions (2023) · 12 citations (OpenAlex)
arXiv:2308.15443 · PDF · DOI · OpenAlex · Extracted main text
Probabilistic price forecasting has recently gained attention in power trading because decisions based on such predictions can yield significantly higher profits than those made with point forecasts alone. At the same time, methods are being developed to combine predictive distributions, since no model is perfect and averaging generally improves forecasting performance. In this article we address the question of whether using CRPS learning, a novel weighting technique minimizing the continuous ranked probability score (CRPS), leads to optimal decisions in day-ahead bidding. To this end, we conduct an empirical study using hourly day-ahead electricity prices from the German EPEX market. We find that increasing the diversity of an ensemble can have a positive impact on accuracy. At the same time, the higher computational cost of using CRPS learning compared to an equal-weighted aggregation of distributions is not offset by higher profits, despite significantly more accurate predictions.
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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 | Marcjasz, G., Narajewski, M., Weron, R., and Ziel, F (2023) Distributional neural networks for electricity price forecasting self | 1.000 | 15 | 4 | 100% |
| 2 | Gneiting, T., and Raftery, A. E (2007) Strictly proper scoring rules, prediction, and estimation | 0.928 | 4 | 3 | 100% |
| 3 | Berrisch, J., and Ziel, F (2023) Multivariate probabilistic CRPS learning with an application to day-ahead electricity prices, 2023 | 0.874 | 6 | 2 | 100% |
| 4 | Berrisch, J., and Ziel, F (2021) CRPS learning | 0.811 | 4 | 2 | 100% |
| 5 | Maciejowska, K., Uniejewski, B., and Weron, R (2023) Forecasting electricity prices self | 0.811 | 4 | 2 | 100% |
| 6 | Gneiting, T., and Katzfuss, M (2014) Probabilistic forecasting | 0.737 | 3 | 2 | 100% |
| 7 | Lichtendahl, K. C., Grushka-Cockayne, Y., and Winkler, R. L (2013) Is it better to average probabilities or quantiles? | 0.737 | 3 | 2 | 100% |
| 8 | Uniejewski, B (2023) Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices, 2023 | 0.737 | 3 | 2 | 100% |
| 9 | Wang, X., Hyndman, R., Li, F., and Kang, Y (2022) Forecast combinations: An over 50-year review | 0.737 | 3 | 2 | 100% |
| 10 | Janczura, J., and Puć, A (2023) ARX-GARCH probabilistic price forecasts for diversification of trade in electricity marketsvariance stabilizing transformation a… | 0.644 | 2 | 2 | 100% |
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