标题： 通过捕捉粗粒度的变量内和变量间依赖关系进行多变量时间序列异常检测 显示英文标题

标题： Multivariate Time Series Anomaly Detection by Capturing Coarse-Grained Intra- and Inter-Variate Dependencies

摘要： 多变量时间序列异常检测对于网络应用操作中的故障管理至关重要，因为它直接影响实施补救或预防措施的有效性和及时性。 这项任务通常被表述为一种半监督学习问题，其中仅可用正常数据进行模型训练，主要是由于数据标记的劳动密集性以及异常数据的稀缺性。 现有的半监督方法通常通过捕捉单变量的时间依赖性和/或变量间的关系来学习正常模式，将偏离这些模式的时间戳标记为异常。 然而，由于这些方法过于关注细粒度，往往无法捕捉时间序列中显著的单变量时间和变量间依赖关系，导致性能不佳。 在本研究中，我们引入了MtsCID，一种新型的半监督多变量时间序列异常检测方法。 MtsCID采用双网络架构：一个网络在多尺度单变量块的注意力图上运行，以粗粒度学习时间依赖性，另一个网络则在变量上运行，通过卷积和与正弦原型的交互来捕捉粗粒度的变量间关系。 这种设计增强了从单变量时间依赖性和变量间关系中捕捉模式的能力，从而提高了性能。 在七个广泛使用的数据集上的大量实验表明，MtsCID的性能可与最先进的基准方法相媲美或更优。 显示英文摘要

摘要： Multivariate time series anomaly detection is essential for failure management in web application operations, as it directly influences the effectiveness and timeliness of implementing remedial or preventive measures. This task is often framed as a semi-supervised learning problem, where only normal data are available for model training, primarily due to the labor-intensive nature of data labeling and the scarcity of anomalous data. Existing semi-supervised methods often detect anomalies by capturing intra-variate temporal dependencies and/or inter-variate relationships to learn normal patterns, flagging timestamps that deviate from these patterns as anomalies. However, these approaches often fail to capture salient intra-variate temporal and inter-variate dependencies in time series due to their focus on excessively fine granularity, leading to suboptimal performance. In this study, we introduce MtsCID, a novel semi-supervised multivariate time series anomaly detection method. MtsCID employs a dual network architecture: one network operates on the attention maps of multi-scale intra-variate patches for coarse-grained temporal dependency learning, while the other works on variates to capture coarse-grained inter-variate relationships through convolution and interaction with sinusoidal prototypes. This design enhances the ability to capture the patterns from both intra-variate temporal dependencies and inter-variate relationships, resulting in improved performance. Extensive experiments across seven widely used datasets demonstrate that MtsCID achieves performance comparable or superior to state-of-the-art benchmark methods.