Abstract: Time-series anomaly detection is a critical task with significant impact as it serves a pivotal role in the field of data mining and quality management. Current anomaly detection methods are ...

In this study, we applied the anomaly detection method based on sparse structure learning of the element correlation within MD trajectories to identify important features associated with state ...

Domain Adaptation Contrastive learning model for Anomaly Detection in multivariate time series (DACAD), combining UDA with contrastive learning. DACAD utilizes an anomaly injection mechanism that ...

Abstract: Anomaly detection in multivariate time series is of significance in industrial equipment fault detection, network security, etc. In light of the arrival of big data, the temporal dependency ...