Lent Arnold

Objective: Ideal anomaly detection algorithms should detect both sudden and gradual changes, while keeping the background false positive alert rate at a tolerable level. Our objective was to develop an anomaly detection algorithm that adapts to the time series being analyzed and reduces false positive signals. Background: Earlier we have presented studies with HWR, where the alerts were generated using a logical OR of several different criteria [1]. The anomaly detection contest required a continuous score for each day of the time series. This gave the impetus to develop a new version of our algorithm.

Ideal anomaly detection algorithms shoulddetect both sudden and gradual changes, while keeping the background false positive alert rate at a tolerable level. The algorithms should also be easy to use. Our objective was to develop an anomaly detection algorithm that adapts to the time series being analyzed and reduces false positive signals.

Effective anomaly detection depends on the timely, asynchronous generation of anomalies from multiple data streams using multiple algorithms. Our objective is to describe the use of a case manager tool for combining anomalies into cases, and for collaborative investigation and disposition of cases, including data visualization.