Correlation-Aware Spatial-Temporal Graph Learning for Multivariate Time-Series Anomaly Detection

Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Khoa Tran Phan, Shirui Pan, Yi-Ping Phoebe Chen, Wei Xiang

Abstract: Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches for this problem mostly employ either statistical models which cannot capture the nonlinear relations well or conventional deep learning (DL) models [e.g., convolutional neural network (CNN) and long short-term memory (LSTM)] that do not explicitly learn the pairwise correlations among variables. To overcome these limitations, we propose a novel method, correlation-aware spatial–temporal graph learning (termed CST-GL ), for time-series anomaly detection. CST-GL explicitly captures the pairwise correlations via a multivariate time series correlation learning (MTCL) module based on which a spatial–temporal graph neural network (STGNN) can be developed. Then, by employing a graph convolution network (GCN) that exploits one- and multihop neighbor information, our STGNN component can encode rich spatial information from complex pairwise dependencies between variables. With a temporal module that consists of dilated convolutional functions, the STGNN can further capture long-range dependence over time. A novel anomaly scoring component is further integrated into CST-GL to estimate the degree of an anomaly in a purely unsupervised manner. Experimental results demonstrate that CST-GL can detect and diagnose anomalies effectively in general settings as well as enable early detection across different time delays. Our code is available at https://github.com/huankoh/CST-GL .