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Ranking probabilistic forecasting models with different loss functions

Tomasz Serafin, Bartosz Uniejewski

arXiv 24 Nov 2024 · Econometrics

arXiv:2411.17743 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In this study, we introduced various statistical performance metrics, based on the pinball loss and the empirical coverage, for the ranking of probabilistic forecasting models. We tested the ability of the proposed metrics to determine the top performing forecasting model and investigated the use of which metric corresponds to the highest average per-trade profit in the out-of-sample period. Our findings show that for the considered trading strategy, ranking the forecasting models according to the coverage of quantile forecasts used in the trading hours exhibits a superior economic performance.

Citation extraction

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Uniejewski, B (2024) Smoothing quantile regression averaging: A new approach to probabilistic forecasting of electricity prices self1.00073100%
2Maciejowska, K., Serafin, T., Uniejewski, B (2024) Probabilistic forecasting with a hybrid factor-qra approach: Application to electricity trading self1.00064100%
3Gneiting, T., Raftery, A (2007) Strictly proper scoring rules, prediction, and estimation0.84333100%
4Maciejowska, K., Uniejewski, B., Weron, R (2023) Forecasting electricity prices self0.73732100%
5Yardley, E., Petropoulos, F (2021) Beyond error measures to the utility and cost of the forecasts0.64422100%
6Kath, C., Ziel, F (2021) Conformal prediction interval estimation and applications to day-ahead and intraday power markets0.51121100%
7Chatfield, C (1993) Calculating interval forecasts0.40511100%
8Fernandes, M., Guerre, E., Horta, E (2021) Smoothing quantile regressions0.40511100%
gia:bun:17unmatched citation key gia:bun:170.40511100%
10Grushka-Cockayne, Y., Lichtendahl Jr, K.C., Jose, V.R.R., Winkler, R.L (2017) Quantile evaluation, sensitivity to bracketing, and sharing business payoffs0.40511100%

Showing the top 10 of 23 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Probabilistic Forecasting for Day-ahead Electricity Prices, Battery Trading Strategies and the Economic Evaluation of Predictive Accuracy0.40511