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2006
Springer

Finding Time Series Discords Based on Haar Transform

12 years 7 months ago
Finding Time Series Discords Based on Haar Transform
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.
Ada Wai-Chee Fu, Oscar Tat-Wing Leung, Eamonn J. K
Added 13 Jun 2010
Updated 13 Jun 2010
Type Conference
Year 2006
Where ADMA
Authors Ada Wai-Chee Fu, Oscar Tat-Wing Leung, Eamonn J. Keogh, Jessica Lin
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