In time series anomaly detection (TSAD), the scarcity of labeled data poses a challenge to the development of accurate models. Unsupervised domain adaptation (UDA) offers a solution by leveraging labeled data from a related domain to detect anomalies in an unlabeled target domain. However, existing UDA methods assume consistent anomalous classes across domains. To address this limitation, we propose a novel 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 enhances generalization across unseen anomalous classes, improving adaptability and robustness. Additionally, our model employs supervised contrastive loss for the source domain and self-supervised contrastive triplet loss for the target domain, ensuring comprehensive feature representation learning and domain-invariant feature extraction. Finally, an effective Center-based Entropy Classifier (CEC) accurately learns normal boundaries in the source domain. Extensive evaluations on multiple real-world datasets and a synthetic dataset highlight DACAD's superior performance in transferring knowledge across domains and mitigating the challenge of limited labeled data in TSAD.

翻译：在时间序列异常检测中，标记数据的稀缺性对开发精确模型构成了挑战。无监督领域自适应通过利用相关领域的标记数据来检测未标记目标领域的异常，为此问题提供了一种解决方案。然而，现有的领域自适应方法假设跨领域的异常类别是一致的。为了解决这一局限性，我们提出了一种新颖的面向多元时间序列异常检测的领域自适应对比学习模型，该模型将领域自适应与对比学习相结合。DACAD采用一种异常注入机制，该机制增强了对未见异常类别的泛化能力，从而提高了适应性和鲁棒性。此外，我们的模型对源域使用监督对比损失，对目标域使用自监督对比三元组损失，确保了全面的特征表示学习和领域不变特征提取。最后，一个有效的基于中心的熵分类器能够准确学习源域中的正常边界。在多个真实世界数据集和一个合成数据集上的广泛评估突显了DACAD在跨领域知识迁移以及缓解时间序列异常检测中标记数据有限挑战方面的卓越性能。