MTS-GAT: multivariate time series anomaly detection based on graph attention networks

Anomaly detection using multivariate time series data from sensors can determine whether the equipment is operating normally. However, anomaly detection suffers from inadequate utilisation of spatio-temporal dependencies and unclear explanations of anomaly causes. To improve the accuracy of anomaly detection and rationalise the causes of anomalies, we propose multivariate time series anomaly detection based on graph attention networks (MTS-GAT). MTS-GAT constructs variable and temporal graphs using embedding vector similarity. The nonlinear dependencies of the variable and temporal dimensions are learned through two parallel graph attention layers. Finally, MTS-GAT jointly optimises the prediction-based and reconstruction-based models. Anomalous variables are localised with the anomaly scores computed after the joint optimisation to enhance the interpretability of anomaly detection. Experimental evaluations prove that MTS-GAT outperforms the best baseline approach, GDN. The F1 scores are improved by 2.73%, 3.39%, and 0.9% on SWaT, WADI, and SMD datasets.