Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series
For practitioners needing reliable uncertainty estimates in graph-structured time series (e.g., traffic forecasting), this work addresses a key limitation of conformal prediction.
The paper tackles uncertainty quantification for graph-structured multivariate time series, where standard conformal prediction fails due to non-exchangeability from cross-node coupling. The proposed SCALE method achieves valid coverage and improves the coverage-efficiency trade-off over state-of-the-art CP methods on real-world traffic datasets.
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. Inspired by the spectral graph theory, such coupling resides in global trends and can be characterized by the low-frequency components, while high-frequency components are nearly exchangeable. Therefore, we propose a novel concept named Spectral Graph Conditional Exchangeability (SGCE), which conditions exchangeable high-frequency components on low-frequency ones to preserve global trends and enable effective CP in the spectral domain. Based on SGCE, we further propose Spectral Conformal prediction via wAveLEt transform (SCALE). SCALE uses graph wavelets to decompose low/high-frequency components and conformalizes high-frequency residuals via adaptive gating over a low-frequency embedding. Experimental results on real-world traffic datasets show that SCALE not only achieves valid coverage but also consistently improves the coverage-efficiency trade-off over the state-of-the-art CP methods.