DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in
  Multivariate Time Series
Zahra Zamanzadeh Darban Geoffrey I. Webb Mahsa Salehi
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.