通过多尺度图对比与稳定性对齐，提升复杂时序数据异常检测精度。
CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection
- 构建局部、区域、全局三层次动态图，跨尺度对比并对齐结构特征。
- 在四个基准上实现最优性能，尤其在PSM和WADI数据集上显著领先。
- 适合工业监控等需高鲁棒性的时序异常检测场景。
多变量时序异常检测对工业控制、遥测和系统监控至关重要。然而，变量间依赖关系的动态演变及噪声干扰使检测困难。现有方法通常采用单尺度图或实例级对比，且学习到的动态图易受噪声影响，缺乏稳定锚点，导致误报或漏报。为此，我们提出CGSTA框架，包含两项创新：首先，动态分层图构建（DLGC）为每个滑动窗口生成局部、区域和全局视角的变量关系图；对比鉴别跨尺度（CDS）不直接对比完整窗口，而是在各视角内对比图表示，并对齐同一窗口在不同视角间的表征，实现结构感知学习。其次，稳定性感知对齐（SAA）为每尺度维护一个基于正常数据学习的稳定参考，并引导当前窗口快速变化的图向其靠拢，抑制噪声。融合多尺度与时间特征，使用条件密度估计器输出逐时刻异常分数。在四个基准数据集上，CGSTA在PSM和WADI上表现最优，在SWaT和SMAP上与基线方法相当。
原文摘要 · Abstract (English)
Multivariate time-series anomaly detection is essential for reliable industrial control, telemetry, and service monitoring. However, the evolving inter-variable dependencies and inevitable noise render it challenging. Existing methods often use single-scale graphs or instance-level contrast. Moreover, learned dynamic graphs can overfit noise without a stable anchor, causing false alarms or misses. To address these challenges, we propose the CGSTA framework with two key innovations. First, Dynamic Layered Graph Construction (DLGC) forms local, regional, and global views of variable relations for each sliding window; rather than contrasting whole windows, Contrastive Discrimination across Scales (CDS) contrasts graph representations within each view and aligns the same window across views to make learning structure-aware. Second, Stability-Aware Alignment (SAA) maintains a per-scale stable reference learned from normal data and guides the current window's fast-changing graphs toward it to suppress noise. We fuse the multi-scale and temporal features and use a conditional density estimator to produce per-time-step anomaly scores. Across four benchmarks, CGSTA delivers optimal performance on PSM and WADI, and is comparable to the baseline methods on SWaT and SMAP.
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