Multi-Layer Collaborative Bandit for Multivariate Time Series Anomaly Detection

Weiyao Zhang, Xuying Meng, Jinyang Li, Yequan Wang, Yujun Zhang

Abstract: Multivariate Time Series Anomaly Detection (MTSAD) detects abnormal indicators from Multivariate Time Series (MTS), and provides the rank of the multiple abnormal indicators to meet the expert's detection interest in current environment, which underpin the security and stability of intelligent cyber-physical systems. However, popular integration-based methods, which are pre-defined, fall short in locating the exact abnormal indicator, nor can they perceive the environmental dynamic and evolve accordingly. Let alone meeting the expert's interest. As a result, the expert's workload is exaggerated. These issues motivate us to propose a novel multi-layer collaborative bandit framework MULA for MTSAD. MULA decomposes MTS and pairs individual time series with a bandit arm, which locates the abnormal indicator directly. Then, MULA sorts the indicators by abnormal scores computed based on the expert's feedback, which facilitates the experts. Besides, to address the adaptability issue, we devise a dual signal to comprehensively monitor environmental changes, and design a multi-layer collaborative mechanism for MULA to adapt to the dynamic environment. Theoretical analysis and experiments on public datasets demonstrate the superiority of MULA compared to the state-of-art.