通过捕获粗粒度的变量内与变量间依赖关系进行多变量时间序列异常检测
Multivariate Time Series Anomaly Detection by Capturing Coarse-Grained Intra- and Inter-Variate Dependencies
AI总结：
提出半监督多变量时间序列异常检测方法 MtsCID，通过双网络架构捕获粗粒度变量内时间依赖和变量间关系，在七个数据集上取得优于或媲美现有基准的性能。
摘要：
多变量时间序列异常检测对于 Web 应用运维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。该任务通常被构建为半监督学习问题，即模型训练仅使用正常数据，这主要是由于数据标注的劳动密集型特性和异常数据的稀缺性。现有的半监督方法通常通过捕获变量内时间依赖关系和/或变量间关系来学习正常模式，从而检测异常，将偏离这些模式的时间戳标记为异常。然而，这些方法由于过度关注过细的粒度，往往无法捕获时间序列中显著的变量内时间依赖和变量间依赖，导致性能次优。在本研究中，我们引入了 MtsCID，一种新颖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：一个网络作用于多尺度变量内分块的注意力图以进行粗粒度时间依赖学习，而另一个网络作用于变量，通过卷积和与正弦原型的交互来捕获粗粒度变量间关系。这种设计增强了从变量内时间依赖和变量间关系中捕获模式的能力，从而提升了性能。在七个广泛使用的数据集上的大量实验表明，MtsCID 实现了与最先进基准方法相当或更优的性能。
英文摘要：
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.
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。
