Explainable Multivariate Time-Series Anomaly Detection via Dynamic Graph Optimal Transport

Multivariate time-series anomaly detection is challenging because anomalies often arise from changes in cross-sensor dependencies rather than large deviations in individual channels. Existing methods capture amplitude errors but often miss these structural anomalies and offer limited support for explainable sensor-level root-cause localization. We propose TOAD, an explainable topology-aware optimal-transport framework for unsupervised multivariate time-series anomaly detection and root-cause attribution. TOAD constructs dynamic sensor graphs, learns topology-aware spatiotemporal latent representations, and detects structural anomalies by comparing latent distributions with regime-aware normal references using sliced Wasserstein distance. To improve robustness, the final anomaly score integrates evidence from transport, reconstruction, and temporal transitions. Beyond detection, TOAD provides faithful sensor-level explanations by attributing anomaly scores through gradient sensitivity, counterfactual score reduction, and graph-aware propagation, enabling root-cause localization directly aligned with the detection mechanism. Extensive experiments on five public benchmarks demonstrate that TOAD achieves the best threshold-independent detection performance on four datasets and improves root-cause localization from 64.0% to 72.8% (H@100) and from 78.2% to 86.4% (H@150), providing accurate and actionable explanations for anomalous events.