[Submitted on 30 Sep 2026]
Title:TRACE: A Reproducible Benchmark for Electricity Price Forecasting with Official Operational Text
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Abstract:Electricity price forecasting (EPF) supports scheduling, bidding, and risk management in electricity markets, yet existing benchmarks focus mainly on numerical inputs, leaving the forecasting value of forecast-time textual context insufficiently evaluated. We introduce TRACE, a reproducible benchmark of 7,300 zone--day instances pairing prices from five zones in a major U.S. market with official operational text available at the forecast cutoff. TRACE reconstructs official operational text at each cutoff, preventing post-cutoff information leakage. We evaluate TRACE for semantic alignment and forecasting value. Semantic assessments align with central movement and both tail risks in ground-truth prices, most consistently for upper-tail price risk. Forecasting value is reflected in a median 7.4\% reduction in upper-tail pinball loss across time-series foundation models. A controlled cross-day text-mismatch ablation reverses the gains, falling below the no-text baseline.
Comments: Accepted to the NeurIPS 2026 Workshop on Foundation Models for Time Series (FMTS)
Subjects:
Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2610.02256 [cs.LG]
(or arXiv:2610.02256v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.02256
arXiv-issued DOI via DataCite
Submission history
From: Xinyi Yi [view email] [v1] Wed, 30 Sep 2026 20:44:33 UTC (162 KB)
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