Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data

TL;DR

This paper introduces the Synchronized Anomaly Agreement Index (SAAI), a new measure leveraging anomaly synchronicity in multivariate time series to better determine the true number of anomaly classes, outperforming existing metrics.

Contribution

The paper presents SAAI, a novel cluster quality measure that incorporates prior knowledge of anomaly synchronicity, improving anomaly class detection and interpretability in multivariate time series.

Findings

Maximizing SAAI improves accuracy in identifying true anomaly classes.

SAAI outperforms Silhouette Score and X-Means in experiments.

Clusters based on SAAI are more interpretable.

Abstract

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.