Multiview Graph Contrastive Learning for Multivariate Time-Series Anomaly Detection in IoT

Shuxin Qin, Lin Chen, Yongcan Luo, Gaofeng Tao

Abstract: Internet of Things (IoT) systems typically generate large amounts of sensory signals that get involved to represent the states of the systems. Most existing methods focus on learning the temporal patterns of the signals to detect anomalies. However, the performance is limited due to two critical problems. First, the relationships between different signals are rarely considered, resulting in missing important information for representation. Second, the high sensitivity of time series makes it difficult to use conventional methods for data augmentation, which limits the improvement of representation and generalization capabilities. In this work, we propose a novel reconstruction-based framework with contrastive learning from multiple views to address these two issues. Specifically, intrasignal and intersignal graph structures are formed and learned in parallel to model the temporal context and capture the dependency relationships between signals, respectively. Multiview graph contrastive learning strategy is designed to improve the graph representation. We also provide an adaptive data augmentation method to generate graph views for contrastive learning, which helps to accurately capture valuable intrinsic patterns from two different perspectives. Finally, the contrastive learning task and the reconstruction task are jointly trained. Extensive experiments on four real-world data sets demonstrate that our method outperforms the existing state-of-the-art baselines.