Multivariate time series anomaly detection based on graph neural network and grated neural network

Abstract

Multivariate time series anomaly detection aims to identify time periods in time series data that are abnormal or deviate from historical data patterns. The increasing dimension of multivariate time series data and the complex temporal dependence relationships make anomaly detection more difficult. Therefore, dealing with the temporal and spatial dependence of the time series data becomes the key to anomaly detection. In this paper, we propose an anomaly detection model GNN-GRUAD based on Graph Neural Network and Gate Recurrent Unit, which can obtain the graph structure of inter-feature dependencies without relying on a priori knowledge, and use it together with the temporal data as the input of the graph neural network to capture the data's spatial dependence relationships. The model also incorporates GRU to model the temporal dependencies of the data, to finally predict the data and achieve anomaly detection based on the prediction error. This paper compares with several other baseline models on the SWaT and WADI datasets, and the model in this paper achieves good anomaly detection performance with an improvement of 0.37% and 7.02% compared to the best baseline on the two publicly available datasets, respectively.