CHIME: CONTEXTUAL HIERARCHICAL INCONSISTENCY MODELING FOR MULTIVARIATE TIME SERIES ANOMALY DETECTION

Reconstruction-based anomaly detection assumes that anomalous observations are harder to reconstruct than normal ones. In multivariate time series, this assumption can fail when anomalies disrupt temporal or inter-variable relationships while remaining accurately reconstructable. This paper presents CHIME, a framework for Contextual Hierarchical Inconsistency Modeling that incorporates historical context into the assessment of cross-scale structural discrepancies. Preceding windows are encoded into context tokens that guide bidirectional cross-attention among fine-, middle-, and coarse-scale representations. Coarse-to-fine attention relates local patterns to broader trends, while fine-to-coarse attention updates coarse representations with local evidence. A variable-relation branch captures dependencies at each resolution. The anomaly score combines reconstruction error with two forms of hierarchical inconsistency: discrepancies between aligned temporal representations and discrepancies between variable-relation representations across scales. Experiments on ten public benchmarks show that CHIME achieves a mean point-adjusted F1 score of 95.54%, ranking first on eight datasets among the compared methods.