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Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and stat ...

A common task in surveillance, scientific discovery and data cleaning involves monitoring routinely collected data for anomalous events. Detecting events in univariate time series data can be effectively accomplished using well-established techniques such as Box-Jenkins models, regression, and stati ...