评估时序大模型在电价预测中的表现，发现其效果依赖外部数据支持。
Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence
- 构建双数据集框架，减少数据污染，公平评估时序大模型
- 大模型在点预测和概率预测上表现优异，但对特征依赖性强
- 与领域专用模型结合可互补，提升整体预测能力
时序基础模型（TSFMs）展现出强大的零样本预测能力，但在依赖协变量、非平稳的场景下泛化能力尚不明确。电力价格预测（EPF）因其复杂的时序依赖性、分布偏移及对结构与上下文信息的强依赖，成为极具挑战性的测试场景。本文提出一个双数据集基准框架，以降低数据污染风险，并实现对TSFMs的公平评估。研究涵盖点预测与概率预测性能、尾部行为、价格尖峰等关键方面，并与领域专用方法进行对比。结果表明，TSFMs具有高度竞争力，常优于通用基线模型，但其表现高度依赖协变量支持，且并未始终超越专为EPF设计的领域特定方法。有趣的是，简单集成TSFMs与领域专用模型显示出显著潜力，提示两类方法可能捕捉互补的预测信息。
原文摘要 · Abstract (English)
Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generalization in covariate-driven, non-stationary settings is underexplored. Electricity price forecasting (EPF) presents a challenging testbed due to complex temporal dependencies, distributional shifts, and strong reliance on structural and contextual information. We propose a two-dataset-benchmarking framework for EPF to mitigate contamination risk and enable fair evaluation of TSFMs. We examine key aspects of EPF including point and probabilistic forecasting performance, tail behavior, price spikes, and comparisons against domain-specific methods. We find that TSFMs are highly competitive and often outperform general-purpose baselines. Yet, their performance depends critically on covariate support, and they do not consistently surpass domain-specific methods tailored to EPF. Interestingly, simple ensembles of TSFMs and domain-specific methods appear to have significant potential, suggesting that the two approaches capture complementary predictive information.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。