EDSR: Extreme Domain Shift Resilience via Causality-Guided Zero-Shot Anomaly Detection

Anomaly detection is significantly challenging when multivariate time series are subjected to extreme domain shifts. These shifts can lead to magnitude variations of up to 100$\times$ between the training phase and deployment, occurring without any labeled anomaly examples for reference, in a zero-shot context. Existing models struggle with substantial domain shifts because they rely on statistical correlations that break down when major changes occur in the distribution. Therefore, we propose a hierarchical causality-guided framework (EDSR) that exploits the invariance of causal relationships; the physical laws remain constant regardless of magnitude scaling. EDSR integrates causal discovery, causality-aware contrastive encoding, gated attention refinement, adversarial manifold alignment, memory-based edge screening, and conditional domain adaptation into a unified pipeline that is resilient to 100$\times$ domain shifts. This hierarchical edge-cloud framework filters 85% of normal data at the edge in under 2 ms, and with end-to-end latency under 26 ms. Empirical evaluation demonstrates performance on synthetic benchmark datasets: VAR-100 and Lorenz-96. The EDSR under 100$\times$ magnitude shifts achieved F1 scores of 35.86% and 45.86%, respectively, outperforming baseline CAROTS method by 186% and 341%. Ablation studies revealed the critical role of adversarial refinement and attention mechanisms, particularly for nonlinear chaos. This study establishes causality as a foundational primitive for zero-shot anomaly detection in safety-critical deployments.