PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Fast online anomaly detection using scan statistics
Ryan Turner, Steven Bottone and Zoubin Ghahramani
In: Machine Learning for Signal Processing (MLSP '10), 29 Aug 2010 - 1 Sep 2010, Kittilä, Finland.

Abstract

We present methods to do fast online anomaly detection using scan statistics. Scan statistics have long been used to detect statistically significant bursts of events. We extend the scan statistics framework to handle many practical issues that occur in application: dealing with an unknown background rate of events, allowing for slow natural changes in background frequency, the inverse problem of finding an unusual lack of events, and setting the test parameters to maximize power. We demonstrate its use on real and synthetic data sets with comparison to other methods.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7767
Deposited By:Ryan Turner
Deposited On:17 March 2011