SCAR: Condition-Aware Retrieval of Local Normal References for Multivariate Time Series Anomaly Detection

Industrial systems frequently switch among operating regimes, and the same local pattern may be normal under one regime yet anomalous under another. The normal reference used for anomaly scoring should therefore vary with operating conditions. Most existing methods, however, still treat the normal reference as a globally uniform object rather than one determined dynamically by query-specific conditions. We propose SCAR (Slow-fast Condition-Aware Retrieval), which dynamically constructs a condition-aware local normal reference for each query and uses it as the primary basis for anomaly scoring. SCAR decomposes each time window into a slowly varying state component and a rapidly varying residual component. It first retrieves regime-consistent candidate windows through state-level retrieval and then selects locally similar normal patches through patch-level context retrieval. SCAR then compares the query against the retrieved reference to obtain a memory distance, combines it with complementary evidence including state novelty and reconstruction diagnostics, and fuses all signals into a final anomaly score via empirical CDF calibration. On five public multivariate time-series anomaly detection benchmarks, SCAR achieves the best AUROC on every dataset as well as the highest average AUROC and AP among 24 baselines. Ablation studies show that this advantage stems from the complete design chain built around condition-aware local normal references rather than from any single component in isolation. Mechanism validation on the TEP dataset, which provides ground-truth regime annotations, further confirms that the learned state space effectively recovers true operating modes and that retrieved normal references predominantly share the same regime as the query.