Hybrid Anomaly Detection via Multihead Dynamic Graph Attention Networks for Multivariate Time Series

In the real world, a large number of multivariate time series data are generated by Internet of Things systems, which are composed of many connected sensing devices. Therefore, it is impractical to consider only a single univariate time series for decision-making. High-dimensional time series decrease the performance of traditional anomaly detection methods. Moreover, many previously developed methods capture temporal correlations instead of spatial correlations. Therefore, it is necessary to learn the temporal and spatial correlations between different time series and timestamps. In this paper, to achieve improved anomaly detection performance for multivariate time series, we propose a novel architecture based on a graph attention network (GAT) with multihead dynamic attention (MDA). This framework simultaneously learns the dependencies between sensors in both the temporal and spatial dimensions. To tackle the overfitting problem in autoencoder (AE)-based methods, we propose a hybrid approach that combines a novel generative adversarial network (GAN) architecture as a reconstruction model with a multilayer perceptron (MLP) as a prediction-based model to detect anomalies together. The detection framework proposed in this paper is called the HAD-multihead dynamic GAT (MDGAT). Extensive experiments on different public benchmarks demonstrate the superior performance of HAD-MDGAT over state-of-the-art methods.