GSLAD：用于多元时间序列异常检测的原型正则化图结构学习

中文摘要 AI 辅助

无监督多元时间序列异常检测方法通常通过预测、重构或表示差异来识别异常。然而，工业故障可能首先改变变量间的结构模式，而单个轨迹仍接近正常，导致异常信号微弱。本文提出GSLAD，一种原型正则化的图结构学习框架，利用结构偏差进行异常评分。GSLAD采用两阶段训练策略。首先，条件感知图学习器和基于图的预测器通过预测监督进行优化。然后，将推断出的正常图聚类为多个结构原型，代表不同的正常运行状态，边级变异性表征结构不确定性。与这些原型的偏差在第二阶段对图学习器进行正则化，鼓励稳定且特定于状态的的结构模式。在推理过程中，将不确定性归一化的结构偏差与预测偏差相结合进行异常评分。在四个工业基准上的实验表明，GSLAD具有强大的整体性能，并证实了结构偏差在异常检测和诊断中的有效性。

英文摘要

Unsupervised multivariate time series anomaly detection methods typically identify anomalies through forecasting, reconstruction, or representation discrepancies. However, industrial faults may first alter inter-variable structural patterns while individual trajectories remain close to normal, resulting in weak anomaly signals. In this paper, we propose GSLAD, a prototype-regularized graph structure learning framework that uses structural deviations for anomaly scoring. GSLAD adopts a two-phase training strategy. First, a condition-aware graph learner and a graph-based forecaster are optimized with predictive supervision. The inferred normal graphs are then clustered into multiple structural prototypes representing different normal operating regimes, with edge-wise variability characterizing structural uncertainty. Deviations from these prototypes regularize the graph learner in the second phase, encouraging stable and regime-specific structural patterns. During inference, uncertainty-normalized structural deviation is combined with predictive deviation for anomaly scoring. Experiments on four industrial benchmarks demonstrate strong overall performance of GSLAD and confirm the effectiveness of structural deviation for anomaly detection and diagnosis.

发表机构

• École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）

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