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REVIEW 2 major objections 1 minor 9 references

Proper scoring rules for probabilistic electricity price forecasts prioritize sharpness over calibration, yielding overconfident uncertainty estimates.

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2026-06-27 17:33 UTC pith:2VYLZN3B

load-bearing objection This is a one-paragraph position statement that asserts proper scoring rules favor sharpness over calibration but supplies no evidence, math, or citations to support it. the 2 major comments →

arxiv 2606.09517 v1 pith:2VYLZN3B submitted 2026-06-08 cs.LG

Investigating Calibration Challenges in Probabilistic Electricity Price Forecasting

Jan Niklas Lettner , Hadeer El Ashhab , Benjamin Sch\"afer This is my paper
classification cs.LG
keywords probabilistic forecastingelectricity pricescalibrationproper scoring rulesuncertainty estimationrenewable energy integrationrisk management
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how standard proper scoring rules used to train and evaluate probabilistic forecasts in electricity markets emphasize sharpness at the expense of calibration. This produces uncertainty estimates that are statistically unreliable and overly confident, turning models into proxies for deterministic point forecasts. With rising renewable energy integration driving market volatility, such forecasts are needed for risk management, yet the reliability gap undermines their practical value. The work concludes that research must move toward calibration-aware objectives and architectures to preserve the distributional integrity of the predictions.

Core claim

Current proper scoring rules often prioritize forecast sharpness at the expense of calibration, leading to overconfident and statistically unreliable uncertainty estimates. Models can become mere proxies for deterministic forecasts when reliability is neglected.

What carries the argument

Proper scoring rules, which evaluate probabilistic forecasts but systematically trade off calibration for sharpness in the electricity price setting.

Load-bearing premise

That the observed prioritization of sharpness over calibration in existing scoring rules is the primary driver of unreliable uncertainty estimates rather than other factors such as data quality or model architecture.

What would settle it

A controlled comparison in which models retrained with an added calibration penalty show measurably higher reliability scores on held-out electricity price data while sharpness remains comparable.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Probabilistic forecasts lose value for risk management in energy markets because their uncertainty bands do not match observed frequencies.
  • Training objectives that ignore calibration push models toward point-forecast behavior even when full distributions are requested.
  • Reliability metrics must be elevated alongside sharpness when designing new forecasting methods for volatile prices.
  • Future architectures should incorporate explicit calibration terms to maintain distributional integrity under increasing renewable penetration.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same scoring-rule bias could affect probabilistic forecasts in other high-volatility domains such as wind or demand prediction.
  • Calibration-aware losses might be combined with existing proper scores without requiring entirely new model families.
  • Empirical tests could measure how much calibration degrades when standard scores are used on datasets with varying renewable shares.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper claims that proper scoring rules used in probabilistic electricity price forecasting often prioritize sharpness over calibration, producing overconfident and unreliable uncertainty estimates. It asserts that models thereby function as proxies for deterministic forecasts when reliability is neglected and concludes that future work must adopt calibration-aware objectives and architectures.

Significance. The topic of calibration versus sharpness in probabilistic forecasting for volatile energy markets is relevant to risk management. However, because the manuscript supplies neither experiments, data, derivations, nor citations, it does not advance understanding or provide evidence that could be assessed for significance.

major comments (2)
  1. [Abstract] Abstract (and full text): the central claim that 'current-proper-scoring rules often prioritize forecast sharpness at the expense of calibration' is stated without any derivation, citation to the literature on proper scoring rules (e.g., CRPS properties), empirical demonstration on electricity-price data, or counter-example. No tables, figures, or quantitative results appear anywhere in the manuscript.
  2. The title announces an 'investigation' into calibration challenges, yet the manuscript consists solely of a one-paragraph position statement containing no methods, experiments, or analysis. This absence directly undermines any claim of demonstration or investigation.
minor comments (1)
  1. [Abstract] The phrase 'current-proper-scoring rules' contains an extraneous hyphen that should be removed for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their review. The manuscript is a concise position statement rather than an empirical study, and we will revise the title, abstract, and framing to reflect this while adding supporting citations to address the identified gaps.

read point-by-point responses

  1. Referee: [Abstract] Abstract (and full text): the central claim that 'current-proper-scoring rules often prioritize forecast sharpness at the expense of calibration' is stated without any derivation, citation to the literature on proper scoring rules (e.g., CRPS properties), empirical demonstration on electricity-price data, or counter-example. No tables, figures, or quantitative results appear anywhere in the manuscript.

    Authors: We acknowledge that the claim is presented without derivation, citations, or empirical support. The manuscript was conceived as a short position piece to flag a potential practical issue in the application of proper scoring rules to volatile electricity prices. We agree this requires substantiation and will add citations to foundational works on proper scoring rules (e.g., Gneiting and Raftery 2007 on CRPS properties) along with a brief theoretical discussion of how optimization under proper scores can still yield overconfident forecasts in finite-sample, high-volatility settings. No new experiments will be added, as the piece remains conceptual. revision: yes

  2. Referee: The title announces an 'investigation' into calibration challenges, yet the manuscript consists solely of a one-paragraph position statement containing no methods, experiments, or analysis. This absence directly undermines any claim of demonstration or investigation.

    Authors: We agree the title is inconsistent with the manuscript's scope. The work is a position statement, not an investigation with methods or analysis. We will revise the title to 'On Calibration Challenges in Probabilistic Electricity Price Forecasting: A Position Statement' and update the abstract and text to explicitly describe the contribution as a conceptual discussion highlighting a gap for future research. revision: yes

Circularity Check

0 steps flagged

No derivation chain or equations; claim is purely observational.

full rationale

The manuscript is a one-paragraph position statement containing no equations, derivations, fitted parameters, self-citations, or load-bearing steps of any kind. The central assertion about scoring rules is presented without proof, counter-example, or reduction to prior inputs. No patterns from the circularity checklist apply because there is no claimed derivation to inspect.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper introduces no new parameters, axioms, or entities; it discusses challenges in existing forecasting evaluation practices.

pith-pipeline@v0.9.1-grok · 5606 in / 990 out tokens · 20032 ms · 2026-06-27T17:33:12.598852+00:00 · methodology

0 comments
read the original abstract

As renewable energy integration increases market volatility, probabilistic electricity price forecasting has become essential for effective risk management. However, current-proper-scoring rules often prioritize forecast sharpness at the expense of calibration, leading to overconfident and statistically unreliable uncertainty estimates. This work highlights the critical gap between theoretical scoring and practical calibration, demonstrating that models can become mere proxies for deterministic forecasts when reliability is neglected. We conclude that future research must shift toward calibration-aware objectives and architectures to ensure the distributional integrity of energy market forecasts.

Figures

Figures reproduced from arXiv: 2606.09517 by Benjamin Sch\"afer, Hadeer El Ashhab, Jan Niklas Lettner.

Figure 1
Figure 1. Figure 1: Illustrative forecast examples showing underdis [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

9 extracted references · 5 canonical work pages · 1 internal anchor

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    Tilmann Gneiting and Matthias Katzfuss. 2014. Probabilistic Forecasting. 1, 1 (2014), 125–151. doi:10.1146/annurev-statistics-062713-085831

    work page doi:10.1146/annurev-statistics-062713-085831 2014

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    Jan Niklas Lettner, Hadeer El Ashhab, Veit Hagenmeyer, and Benjamin Schäfer

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    Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting

    Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting. arXiv:2604.14739 [cs.LG] https: //arxiv.org/abs/2604.14739

    work page internal anchor Pith review Pith/arXiv arXiv

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    2022.Forecasting Electricity Prices

    Katarzyna Maciejowska, Bartosz Uniejewski, and Rafał Weron. 2022.Forecasting Electricity Prices. arXiv:2204.11735 [q-fin] doi:10.48550/arXiv.2204.11735

    work page doi:10.48550/arxiv.2204.11735 2022

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    Jakub Nowotarski and Rafał Weron. 2015. Computing Electricity Spot Price Prediction Intervals Using Quantile Regression and Forecast Averaging. 30, 3 (2015), 791–803. doi:10.1007/s00180-014-0523-0

    work page doi:10.1007/s00180-014-0523-0 2015

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    Sebastian Pütz, Hadeer El Ashhab, Matthias Hertel, Ralf Mikut, Markus Götz, Veit Hagenmeyer, and Benjamin Schäfer. 2024. Feasibility of Forecasting Highly Resolved Power Grid Frequency Utilizing Temporal Fusion Transformers. InPro- ceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems (E-Energy ’24). Association for Co...

    work page doi:10.1145/3632775.3661963 2024

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    InProceedings of the 40th International Conference on Machine Learning(2023-07-03)

    Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows. InProceedings of the 40th International Conference on Machine Learning(2023-07-03). PMLR, 31732–31753. https://proceedings.mlr.press/v202/ si23a.html

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