面向多元时间序列异常检测的耦合注意力网络
Coupled Attention Networks for Multivariate Time Series Anomaly Detection
AI总结：
针对多元时间序列中传感器变量依赖动态变化的异常检测难题，提出融合自适应图学习与图注意力的耦合注意力网络CAN，结合多级编解码架构，在真实数据集上性能显著优于现有最优基线。
摘要：
多元时间序列异常检测（MTAD）在众多现实应用领域中发挥着至关重要的作用。过去几年间，MTAD 吸引了学术界与工业界日益增长的关注。研究人员已开发出众多深度学习与图学习模型，用于在多元时间序列数据中开展有效异常检测，为智能监控、风险管理等高级应用提供了前所未有的能力。尽管如此，MTAD 仍面临着由传感器与变量间的依赖关系带来的严峻挑战，这些依赖关系往往会随时间发生变化。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，耦合注意力网络），用于处理具有动态变量关系的多元时间序列数据的异常检测任务。我们将自适应图学习方法与图注意力相结合，生成一种全局-局部图，该图能够同时表征传感器之间的全局相关性与动态局部相关性。为捕捉传感器间关系与时间依赖关系，我们将基于全局-局部图的卷积神经网络与时间自注意力模块相融合，构建出耦合注意力模块。此外，我们还开发了一种支持重建与预测任务的多级编码器-解码器架构，以更好地刻画多元时间序列数据的特征。我们在多个真实世界数据集上开展了大量实验以评估所提 CAN 方法的性能，结果表明 CAN 的表现显著优于当前最优的基线模型。
英文摘要：
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.
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。
