GDCMAD: Graph-based dual-contrastive representation learning for multivariate time series anomaly detection

Abstract

The increasing amount of multivariate time series (MTS), coupled with scarce labeled samples, has driven the development of unsupervised anomaly detection. While contrastive learning has shown promise in learning discriminative representations, existing contrastive learning-based MTS anomaly detection methods still suffer from limited representation power and inadequate discrimination ability. In this paper, we propose a novel model, graph-based dual-contrastive representation learning for detecting anomalies in multivariate time series, called GDCMAD. GDCMAD first constructs two relational graphs for capturing inter-variable and temporal dependencies then integrates an improved Kolmogorov–Arnold network (KAN)-based attention mechanism into a reconstruction framework. Additionally, it incorporates an LSTM-based external contrastive learning module to further enhance the separation between normal and abnormal patterns. Experiments on six public datasets show that GDCMAD achieves better performance than nine state-of-the-art methods in detecting anomalies, confirming its effectiveness for MTS data. To access the source code of GDCMAD, please visit the repository located at https://github.com/Du-Team/GDCMAD.