AI 中文总结
针对电价预测中时间序列基础模型在协变量驱动、非平稳设置下泛化不足的问题，提出双数据集基准框架，评估其多方面表现，发现该模型有竞争力但性能依赖协变量支持，简单集成有潜力。
AI 中文摘要
时间序列基础模型（TSFMs）已展现出强大的零样本预测性能，但其在协变量驱动的非平稳环境中的泛化能力尚未得到充分探索。由于复杂的时间依赖性、分布变化以及对结构和上下文信息的强烈依赖，电价预测（EPF）提供了一个具有挑战性的测试平台。我们提出了一个用于EPF的双数据集基准框架，以减轻污染风险并实现对TSFMs的公平评估。我们研究了EPF的关键方面，包括点预测和概率预测性能、尾部行为、价格峰值以及与特定领域方法的比较。我们发现TSFMs具有高度竞争力，并且通常优于通用基线。然而，它们的性能严重依赖于协变量支持，并且它们并不能始终超过针对EPF量身定制的特定领域方法。有趣的是，TSFMs和特定领域方法的简单集成似乎具有巨大潜力，这表明这两种方法捕获了互补的预测信息。
英文摘要
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.
Journal refICML 2026 Foundation Models for Structured Data Workshop, 43rd International Conference on Machine Learning (ICML), Seoul, South Korea
