对比检测：面向无监督多变量时间序列异常检测的动态图对比正则化
Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series
AI总结：
提出ContrastAD框架，通过动态图对比学习将结构演化转化为学习信号，结合多视角编码器和频率感知注意力混合器，实现无监督多变量时间序列异常检测。
摘要：
多变量时间序列（MTS）中的异常检测受到动态变量间依赖关系和频谱噪声下特征纠缠的阻碍，在实践中，由于缺乏异常标签而进一步复杂化。现有的基于重构的检测器倾向于像正常模式一样忠实地恢复异常，而流行的图对比方法强制视图间不变性，从而假设一个平稳的关系结构，这一假设在真实系统的结构漂移下被打破。我们提出ContrastAD，一个无监督框架，将结构演化本身转变为学习信号而非抑制它。一个多视角编码器从时间、属性和结构视角编码输入。一个频率感知注意力混合器在注意力之前执行频谱top-K过滤，防止噪声泄漏到查询-键相似度中。核心组件，一个动态图对比学习器，从批次级DTW距离构建基于幂律的稀疏图快照，并将最发散的对与稳定锚点进行对比，在不施加刚性不变性的情况下正则化潜在空间。在五个真实世界基准上，ContrastAD在所有五个数据集上获得最高平均F1，并在三个数据集上获得最高AUC（SWaT 93.60，SMD 98.66，PSM 97.79），在SWaT和PSM上相对于最强基线具有统计显著的F1和AUC差距。在MSL和SMAP上，其AUC落后领先者不到0.7个百分点，同时F1仍领先。消融和敏感性研究进一步证实，对比目标作为软正则化器效果最佳，支持我们的主张：在非平稳动态下严格不变性是次优的。
英文摘要：
Anomaly detection in multivariate time series (MTS) is hindered by dynamic inter-variable dependencies and feature entanglement under spectral noise, and in practice, is further complicated by the absence of anomaly labels. Existing reconstruction-based detectors tend to recover anomalies as faithfully as normal patterns, while prevailing graph contrastive methods enforce invariance across views and thus assume a stationary relational structure, an assumption that breaks under structural drift in real systems. We propose ContrastAD, an unsupervised framework that turns structural evolution itself into a learning signal rather than suppressing it. A Multi-Perspective Embedder encodes inputs from temporal, attribute, and structural perspectives. A Frequency-Aware Attention Mixer then performs spectral top-K filtering before attention, preventing noise from leaking into query-key similarities. The core component, a Dynamic Graph Contrastive Learner, builds power-law-inspired sparse graph snapshots from batch-level DTW distances and contrasts the most divergent pair against a stable anchor, regularizing the latent space without imposing rigid invariance. Across five real-world benchmarks, ContrastAD attains the highest mean F1 on all five datasets and the highest AUC on three (SWaT 93.60, SMD 98.66, PSM 97.79), with statistically significant F1 and AUC margins over the strongest baseline on SWaT and PSM. On MSL and SMAP, it trails the AUC leader by under 0.7 points while still leading on F1. Ablation and sensitivity studies further confirm that the contrastive objective works best as a soft regularizer, supporting our claim that strict invariance is suboptimal under non-stationary dynamics.
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