mlsanityData to 14 Oct 2026

TRACE: A Reproducible Benchmark for Electricity Price Forecasting with Official Operational Text

arXiv 2610.02256 ↗First posted 30 Sep 2026cs.LG stat.MLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 30 Sep 2026.

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18 of 34 references.

References · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  3. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 1,040
  4. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
  5. 2024Chronos: Learning the Language of Time Seriescited by 759
  6. 2023A decoder-only foundation model for time-series forecastingcited by 692
  7. 2024TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variablescited by 248
  8. 2025Chronos-2: From Univariate to Universal Forecastingcited by 194
  9. 2025Sundial: A Family of Highly Capable Time Series Foundation Modelscited by 180
  10. 2020Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmarkcited by 108
  11. 2024Context is Key: A Benchmark for Forecasting with Essential Textual Informationcited by 87
  12. 2024Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysiscited by 74
6 more
  1. 2025Moirai 2.0: When Less Is More for Time Series Forecastingcited by 69
  2. 2025Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Seriescited by 19
  3. 2025Fidel-TS: A High-Fidelity Multimodal Benchmark for Time Series Forecastingcited by 10
  4. 2023A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Marketcited by 7
  5. 2026From Text to Forecasts: Bridging Modality Gap with Temporal Evolution Semantic Spacecited by 5
  6. 2025NSW-EPNews: A News-Augmented Benchmark for Electricity Price Forecasting with LLMscited by 1

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