提出新指标SAAI，提升多变量时间序列异常聚类的准确性与可解释性。
Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data
- 利用异常同步性设计新聚类评估指标SAAI
- SAAI比SSC和X-Means提升0.23和0.32的准确率
- 结果更易解释，适合故障诊断场景
检测与分类异常系统状态对状态监控至关重要，但监督方法常因异常稀少和标签数据不足而效果有限。因此，聚类常被用于分组相似异常行为。然而，在无真实标签的情况下评估聚类质量颇具挑战，现有指标如轮廓系数（SSC）仅关注簇内紧密度与簇间分离度，忽略数据可能存在的先验知识。为此，我们提出同步异常一致指数（SAAI），利用多变量时间序列中异常的同步性来评估聚类质量。实验表明，最大化SAAI相较于SSC在识别真实异常类别数K的任务上提升0.23准确率，相较于X-Means提升0.32。同时，SAAI生成的聚类更易解释。
原文摘要 · Abstract (English)
Detecting and classifying abnormal system states is critical for condition monitoring, but supervised methods often fall short due to the rarity of anomalies and the lack of labeled data. Therefore, clustering is often used to group similar abnormal behavior. However, evaluating cluster quality without ground truth is challenging, as existing measures such as the Silhouette Score (SSC) only evaluate the cohesion and separation of clusters and ignore possible prior knowledge about the data. To address this challenge, we introduce the Synchronized Anomaly Agreement Index (SAAI), which exploits the synchronicity of anomalies across multivariate time series to assess cluster quality. We demonstrate the effectiveness of SAAI by showing that maximizing SAAI improves accuracy on the task of finding the true number of anomaly classes K in correlated time series by 0.23 compared to SSC and by 0.32 compared to X-Means. We also show that clusters obtained by maximizing SAAI are easier to interpret compared to SSC.
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