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Detecting Anomalies in a Time Series Database

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Executive Summary

This paper presents a comprehensive evaluation of a large number of semi-supervised anomaly detection techniques for time series data. Some of these are existing techniques and some are adaptations that have never been tried before. For example, the authors adapt the window based discord detection technique to solve this problem. This paper also investigates several techniques that detect anomalies in discrete sequences, by discretizing the time series data. The authors evaluate these techniques on a large variety of data sets obtained from a broad spectrum of application domains.

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