Multivariate Time Series (MTS) anomaly detection focuses on pinpointing samples that diverge from standard operational patterns, which is crucial for ensuring the safety and security of industrial applications. The primary challenge in this domain is to develop representations capable of discerning anomalies effectively. The prevalent methods for anomaly detection in the literature are predominantly reconstruction-based and predictive in nature. However, they typically concentrate on a single-dimensional instance level, thereby not fully harnessing the complex associations inherent in industrial MTS. To address this issue, we propose a novel self-supervised hierarchical contrastive consistency learning method for detecting anomalies in MTS, named HCL-MTSAD. It innovatively leverages data consistency at multiple levels inherent in industrial MTS, systematically capturing consistent associations across four latent levels-measurement, sample, channel, and process. By developing a multi-layer contrastive loss, HCL-MTSAD can extensively mine data consistency and spatio-temporal association, resulting in more informative representations. Subsequently, an anomaly discrimination module, grounded in self-supervised hierarchical contrastive learning, is designed to detect timestamp-level anomalies by calculating multi-scale data consistency. Extensive experiments conducted on six diverse MTS datasets retrieved from real cyber-physical systems and server machines, in comparison with 20 baselines, indicate that HCL-MTSAD's anomaly detection capability outperforms the state-of-the-art benchmark models by an average of 1.8\% in terms of F1 score.

翻译：多元时间序列（MTS）异常检测关注识别偏离标准运行模式的样本，这对保障工业应用的安全性与可靠性至关重要。该领域的主要挑战在于开发能够有效判别异常的表征方法。现有文献中主流的异常检测方法本质上可分为重建型与预测型两类，但通常聚焦于单维度实例级分析，未能充分挖掘工业MTS中固有的复杂关联。针对该问题，我们提出一种新颖的自监督分层对比一致性学习方法（命名为HCL-MTSAD），用于检测MTS中的异常。该方法创新性地利用工业MTS中多层次数据一致性，系统性地捕获四个潜在层级（测量层、样本层、通道层与流程层）的一致关联。通过设计多层对比损失函数，HCL-MTSAD能够深度挖掘数据一致性与时空关联，从而生成更具信息量的表征。在此基础上，基于自监督分层对比学习构建异常判别模块，通过计算多尺度数据一致性实现时间戳级异常检测。在来自真实信息物理系统与服务器主机的六个多样化MTS数据集上进行的广泛实验（与20个基线模型对比）表明，HCL-MTSAD的异常检测能力在F1分数上平均超越现有最优基准模型1.8%。