ReCATS: Replay-Free Continual Anomaly Detection for Non-Stationary Multivariate Time Series

Abstract

Multivariate time series anomaly detection is critical in safety-critical domains such as industrial monitoring and financial systems. However, real-world time series are inherently non-stationary, with evolving data distributions driven by changing operational regimes and system dynamics. As a result, most existing methods, which assume static data distributions, exhibit severe performance degradation over time and are prone to catastrophic forgetting when incrementally updated. To address these challenges, we propose ReCATS, a replay-free framework for continual anomaly detection in non-stationary multivariate time series. ReCATS combines multi-regime modeling with a dual-phase generative alignment mechanism, enabling effective adaptation to distribution shifts while preserving knowledge acquired from past tasks. Furthermore, we introduce a dynamic dual-scale thresholding strategy to mitigate decision boundary drift under evolving data distributions. Extensive experiments on six real-world benchmark datasets demonstrate that ReCATS consistently outperforms state-of-the-art methods in terms of detection accuracy, knowledge retention, and transferability, as evaluated by standard continual learning metrics, including Backward Transfer and Forward Transfer. The code is available at https://github.com/Li-Qiuyang/ReCATS.