研究人员开发了一种方法，以提高时序基础模型（TSFM）在电网等关键基础设施应用中的透明度。他们的方法使用 Shapley Additive Explanations (SHAP) 来解释模型预测，方法是选择性地隐藏输入，从而实现可扩展的分析。对日前负荷预测的评估表明，Chronos-2 和 TabPFN-TS 等 TSFM 表现具有竞争力，并且它们的解释与领域知识一致，证明了它们作为可靠工具的潜力。 AI
影响 增强了对关键基础设施预测中人工智能的信任，从而能够在能源系统中更广泛地采用。
排序理由 关于时序基础模型可解释性的学术论文。
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arXiv:2604.28149v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in energy systems. However, applications in critical infrastructure like power grids …
Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in energy systems. However, applications in critical infrastructure like power grids require transparency to ensure trust and reliabi…