用周期图结构捕捉时间序列动态关联，提升异常检测精度。
Periodic Graph-Enhanced Multivariate Time Series Anomaly Detector
- 基于FFT的周期时隙分配，动态构建时变图结构
- 在四个真实数据集上优于现有最优模型
- 适合工业监控、金融风控等需捕捉复杂时序模式的场景
多变量时间序列（MTS）异常检测广泛应用于金融、医疗和工业监测等领域。然而，现有方法多基于静态图结构，难以准确刻画MTS中复杂的时空相关性。为此，本文提出周期图增强的多变量时间序列异常检测器（PGMA），包含两个核心思想：(a) 基于快速傅里叶变换（FFT）设计周期时隙分配策略，使图结构能反映MTS的动态变化；(b) 利用图神经网络与时间扩展卷积，从重构的周期图中精准提取复杂时空相关性。在四个真实应用数据集上的实验表明，所提PGMA在多变量时间序列异常检测任务中优于现有最先进模型。
原文摘要 · Abstract (English)
Multivariate time series (MTS) anomaly detection commonly encounters in various domains like finance, healthcare, and industrial monitoring. However, existing MTS anomaly detection methods are mostly defined on the static graph structure, which fails to perform an accurate representation of complex spatio-temporal correlations in MTS. To address this issue, this study proposes a Periodic Graph-Enhanced Multivariate Time Series Anomaly Detector (PGMA) with the following two-fold ideas: a) designing a periodic time-slot allocation strategy based Fast Fourier Transform (FFT), which enables the graph structure to reflect dynamic changes in MTS; b) utilizing graph neural network and temporal extension convolution to accurate extract the complex spatio-temporal correlations from the reconstructed periodic graphs. Experiments on four real datasets from real applications demonstrate that the proposed PGMA outperforms state-of-the-art models in MTS anomaly detection.
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