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.

本文提出了一种结合无监督域自适应和对比表示学习的新颖多变量时序异常检测领域适应对比学习模型（DACAD），模型通过引入各种类型的合成异常来增强泛化能力，改善领域间未见异常类的一般化，提出了针对异常检测的有效中心熵分类器（CEC），有效学习源域中正常边界，广泛的实证验证结果表明 DACAD 在多个现实世界数据集上优于其他领先的时序异常检测和无监督域自适应模型，同时可以缓解有限标注数据对时序异常检测的挑战。