Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increasing attention from both academia and industry. Many deep learning and graph learning models have been developed for effective anomaly detection in multivariate time series data, which enable advanced applications such as smart surveillance and risk management with unprecedented capabilities. Nevertheless, MTAD is facing critical challenges deriving from the dependencies among sensors and variables, which often change over time. To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring dynamic variable relationships. We combine adaptive graph learning methods with graph attention to generate a global-local graph that can represent both global correlations and dynamic local correlations among sensors. To capture inter-sensor relationships and temporal dependencies, a convolutional neural network based on the global-local graph is integrated with a temporal self-attention module to construct a coupled attention module. In addition, we develop a multilevel encoder-decoder architecture that accommodates reconstruction and prediction tasks to better characterize multivariate time series data. Extensive experiments on real-world datasets have been conducted to evaluate the performance of the proposed CAN approach, and the results show that CAN significantly outperforms state-of-the-art baselines.

翻译：多变量时间序列异常检测（MTAD）在众多实际应用领域中发挥着关键作用。近年来，MTAD引起了学术界和工业界的广泛关注。许多深度学习和图学习模型已被开发用于多变量时间序列数据的有效异常检测，从而实现了智能监控和风险管理等具有前所未有能力的先进应用。然而，MTAD面临源于传感器和变量间依赖关系的重大挑战，这些依赖关系通常随时间变化。为解决这一问题，我们提出了一种基于耦合注意力的神经网络框架（CAN），用于具有动态变量关系的多变量时间序列数据中的异常检测。我们将自适应图学习方法与图注意力相结合，生成一个能够表示传感器间全局相关性和动态局部相关性的全局-局部图。为了捕获传感器间关系和时间依赖性，基于全局-局部图的卷积神经网络与时间自注意力模块集成，构建了一个耦合注意力模块。此外，我们开发了一个多级编码器-解码器架构，该架构同时支持重建和预测任务，以更好地表征多变量时间序列数据。在真实数据集上进行了大量实验以评估所提出的CAN方法的性能，结果表明CAN显著优于最先进的基线方法。