AI RESEARCH
Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
arXiv CS.LG
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ArXi:2604.28149v1 Announce Type: new 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 reliability and cannot rely on pure black-box models. To enhance the transparency of TSFMs, we propose an efficient algorithm for computing Shapley Additive Explanations (SHAP) tailored to these models.