Global-Local Association Discrepancy for Multivariate Time Series Anomaly Detection in IIoT

Detecting anomalies in multivariate time series (MTS) data collected from industrial Internet of Things (IIoT) systems is essential for a variety of applications, including smart manufacturing. Existing methods typically learn local spatiotemporal representations from nearby time points and neighboring nodes to reconstruct or predict sensor data. However, these local representations are insufficient to model the complex nonlinear topological relationships and dynamic temporal patterns of IIoT systems, which often results in a high-false alarm rate. To address this issue, we propose a new MTS anomaly detection framework called GLAD, which is based on the global-local association discrepancy. The key concept is to detect anomalies based on the difference between the global and local spatiotemporal associations of each data sample, as the association distribution of each data sample provides a more informative description. Specifically, we introduce a Gumbel-Softmax-based graph structure learning strategy to capture the global topological connections from data. Based on the topological graph structure, we utilize a graph attention network (GAT) and transformer to extract both the global and local spatiotemporal associations of each data sample. Finally, we leverage the global-local association discrepancy to effectively detect anomalies from normal data samples. Extensive experiments on five real-world data sets demonstrate the superiority of GLAD over other state-of-the-art methods.