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Ideal anomaly detection algorithms should detect both sudden and gradual changes, while keeping the background false positive alert rate at a tolerable level. Further, the algorithm needs to perform well when the need is to detect small outbreaks in low-incidence diseases. For example, when... Read more

Content type: Abstract

Objective

Several authors have described ways to introduce artificial outbreaks into time series for the purpose of developing, testing, and evaluating the effectiveness and timeliness of anomaly detection algorithms, and more generally, early event detection systems. While the... Read more

Content type: Abstract

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... Read more

Content type: Abstract

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