Anomaly Detection for Multivariate Time Series Based on Contrastive Learning and Autoformer

Nowadays, due to the increasingly complex and huge data flow, anomaly detection has become an important technology to ensure the high security and reliability of the systems. It helps identify unusual observations in the data, thereby protecting safety and reducing risk. However, in the field of multivariate temporal anomaly detection, there are still some difficulties in spatio-temporal correlation mining of variables, generalization ability in dealing with complex temporal sequences, and feature learning under different granularity. Therefore, we propose an unsupervised anomaly detection method for multivariate time series based on contrastive learning and Autoformer autoencoder. In order to fully explore the spatio-temporal correlation of time series data, this paper uses Graph Attention Network and Auto-Correlation to jointly learn spatio-temporal correlation, and obtain positive and negative pairs through time-domain and frequency-domain data augmentation. Finally, the Autoformer autoencoder is used for anomaly detection in a predictive manner. In the loss function section, a joint loss function is formed by introducing contrastive learning losses at the time step and time window granularity, combined with prediction loss, to improve the learning effect. This paper investigates the performance of the proposed method through experiments on the SMAP and MSL public datasets, and achieves better results than baseline methods.