Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection

Understand

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 non-linear relations well or conventional deep learning models (e.g., CNN and 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 module based on which a spatial-temporal graph neural network (STGNN) can be developed.