Graphs on STAGE for Interpretable Time Series Anomaly Detection

Multivariate time series monitoring is critical for safeguarding complex systems, where anomalies typically stem from interactions among interdependent dimensions. However, current detection paradigms face an inherent trade-off: traditional graph methods offer interpretability, but lag in empirical performance, while modern deep learning models provide high expressiveness at the cost of speed and transparency. We present STAGE, a self-supervised framework combining representation learning with explicit relational structure. STAGE models the time series dimensions as vertices in a dynamic graph, pruning edges via a multi-head attention mechanism to isolate genuine cross-channel dependencies. For explainability, STAGE derives instantaneous, post-hoc attributions by occluding individual dimension nodes and observing the shift in anomaly scores, requiring zero retraining. On the largest benchmark, TSB-AD-M, Stage achieves a VUS-PR of 0.44, matching the best baseline (out of 30) and outperforming the runner-up by 12%. Crucially, it matches both the detection accuracy and diagnostic interpretability of state-of-the-art methods, which cannot provide real-time explanations.