DACAD：用于多变量时间序列异常检测的域自适应对比学习
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
AI总结：
针对时间序列异常检测中标记数据稀缺及跨域异常类别不一致的问题，提出结合无监督域自适应与对比学习的DACAD模型，通过异常注入、双对比损失和中心熵分类器，实现跨域知识迁移与高效异常检测。
摘要：
在时间序列异常检测（TSAD）中，标记数据的稀缺对开发准确模型构成了挑战。无监督域自适应（UDA）通过利用来自相关域的标记数据来检测未标记目标域中的异常，提供了一种解决方案。然而，现有的UDA方法假设跨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用于多变量时间序列异常检测的域自适应对比学习模型（DACAD），将UDA与对比学习相结合。DACAD利用一种异常注入机制，增强了对未见异常类别的泛化能力，提高了适应性和鲁棒性。此外，我们的模型对源域采用监督对比损失，对目标域采用自监督对比三元组损失，确保了全面的特征表示学习和域不变特征提取。最后，一个有效的基于中心的熵分类器（CEC）在源域中准确学习正常边界。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在跨域迁移知识和缓解TSAD中标记数据有限挑战方面的优越性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
