Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series

Dau, Hoang Anh, Silva, Diego Furtado, Petitjean, François, Forestier, Germain, Bagnall, Anthony and Keogh, Eamonn (2017) Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series. In: IEEE International Conference on Big Data. The Institute of Electrical and Electronics Engineers (IEEE), USA, pp. 917-922.

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While the Dynamic Time Warping (DTW) - based Nearest-Neighbor Classification algorithm is regarded as a strong baseline for time series classification, in recent years there has been a plethora of algorithms that have claimed to be able to improve upon its accuracy in the general case. Many of these proposed ideas sacrifice the simplicity of implementation that DTW-based classifiers offer for rather modest gains. Nevertheless, there are clearly times when even a small improvement could make a large difference in an important medical or financial domain. In this work, we make an unexpected claim; an underappreciated “low hanging fruit” in optimizing DTW’s performance can produce improvements that make it an even stronger baseline, closing most or all the improvement gap of the more sophisticated methods. We show that the method currently used to learn DTW’s only parameter, the maximum amount of warping allowed, is likely to give the wrong answer for small training sets. We introduce a simple method to mitigate the small training set issue by creating synthetic exemplars to help learn the parameter. We evaluate our ideas on the UCR Time Series Archive and a case study in fall classification, and demonstrate that our algorithm produces significant improvement in classification accuracy.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Data Science and Statistics
Related URLs:
Depositing User: Pure Connector
Date Deposited: 31 Oct 2017 06:10
Last Modified: 21 Oct 2022 16:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/65311
DOI: 10.1109/BigData.2017.8258009


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