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2000
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Scaling up dynamic time warping for datamining applications

12 years 3 months ago
Scaling up dynamic time warping for datamining applications
There has been much recent interest in adapting data mining algorithms to time series databases. Most of these algorithms need to compare time series. Typically some variation of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow more robust distance calculations, however it is computationally expensive. In this paper we introduce a tion of DTW which operates on a higher level abstraction of the data, in particular, a Piecewise Aggregate Approximation (PAA). Our approach allows us to outperform DTW by one to two orders of magnitude, with no loss of accuracy. Keywords Time series, similarity measures, Dynamic Time Warping.
Eamonn J. Keogh, Michael J. Pazzani
Added 25 Aug 2010
Updated 25 Aug 2010
Type Conference
Year 2000
Where KDD
Authors Eamonn J. Keogh, Michael J. Pazzani
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