DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series

Time series anomaly detection (TAD) faces a significant challenge due to the scarcity of labelled data, which hinders the development of accurate detection models. Unsupervised domain adaptation (UDA) addresses this challenge by leveraging a labelled dataset from a related domain to detect anomalies in a target dataset. Existing domain adaptation techniques assume that the number of anomalous classes does not change between the source and target domains. In this paper, we propose a novel Domain Adaptation Contrastive learning for Anomaly Detection in multivariate time series (DACAD) model to address this issue by combining UDA and contrastive representation learning. DACAD's approach includes an anomaly injection mechanism that introduces various types of synthetic anomalies, enhancing the model's ability to generalise across unseen anomalous classes in different domains. This method significantly broadens the model's adaptability and robustness. Additionally, we propose a supervised contrastive loss for the source domain and a self-supervised contrastive triplet loss for the target domain, improving comprehensive feature representation learning and extraction of domain-invariant features. Finally, an effective Centre-based Entropy Classifier (CEC) is proposed specifically for anomaly detection, facilitating accurate learning of normal boundaries in the source domain. Our extensive evaluation across multiple real-world datasets against leading models in time series anomaly detection and UDA underscores DACAD's effectiveness. The results validate DACAD's superiority in transferring knowledge across domains and its potential to mitigate the challenge of limited labelled data in time series anomaly detection.