Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
Real-world multivariate time series exhibit sparse, unlabeled anomalies, and existing methods suffer from overly complex architectures, coarse detection granularity, and inflated performance estimates. To address these issues, we propose OracleAD—a novel unsupervised framework that jointly models current prediction and historical reconstruction via causal embedding. It introduces Stable Latent Structure (SLS) to encode spatial relationships underlying normal behavior and employs self-attention to capture dynamic spatiotemporal dependencies. OracleAD features a dual-scoring mechanism—combining prediction error and SLS deviation—for fine-grained anomaly detection. Crucially, it localizes root-cause variables violating temporal causality directly at the embedding layer, ensuring both high accuracy and interpretability. Evaluated on multiple real-world datasets under rigorous protocols, OracleAD consistently outperforms state-of-the-art methods. It further supports both anomaly segment identification and critical variable localization.