06. Anomaly Detection using a scaled Bregman Divergence

Anomaly detection is to identify the specific moments when a system exhibits significantly different behavior from its usual patterns. Density ratio estimations, such as the Kullback-Leibler (KL) importance estimation procedure (KLIEP), unconstrained least-squares importance fitting (uLSIF), and relative uLSIF (RuLSIF), have been widely used for the anomaly detection, because estimating the ratio of two probability densities is easier to accomplish than estimating each density separately. However, these methods have many limitations, including the unboundedness and unstable issues. In this work, we propose a novel approach based on a scaled Bregman divergence using a mixture measure, together with the Kernel regression method, for anomaly detection in multivariate time series data. Finally, we apply the proposed method to detect anomalies from simulation data and real-world data.

Mid-Level

Knowledge

Women in Statistics and Data Science 2024