HWR at the Contest: A Holt-Winters-Based Method Applied to Simulated Outbreaks

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.