Finding time series discords based on haar transform

Authors:
Ada Wai-chee Fu;Oscar Tat-Wing Leung;Eamonn Keogh;Jessica Lin
Affiliations:
Department of Computer Science and Engineering, The Chinese University of Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong;Department of Computer Science and Engineering, University of California, River, CA;Department of Information and Software Engineering, George Mason University
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

The problem of finding anomaly has received much attention recently. However, most of the anomaly detection algorithms depend on an explicit definition of anomaly, which may be impossible to elicit from a domain expert. Using discords as anomaly detectors is useful since less parameter setting is required. Keogh et al proposed an efficient method for solving this problem. However, their algorithm requires users to choose the word size for the compression of subsequences. In this paper, we propose an algorithm which can dynamically determine the word size for compression. Our method is based on some properties of the Haar wavelet transformation. Our experiments show that this method is highly effective.