AI RESEARCH
Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series
arXiv CS.LG
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ArXi:2605.04957v1 Announce Type: new Point forecasting for graph-structured multivariate time series is a fundamental problem, but rigorous uncertainty quantification for such predictions is still underexplored. Conformal prediction (CP) offers uncertainty estimation with a solid coverage guarantee under the exchangeability assumption, which requires the joint data distribution to be unchanged under permutation. However, in graph-structured time series, inherent cross-node coupling can violate the exchangeability condition, making direct application of CP unreliable.