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

Hoang Anh Dau, Diego Furtado Silva, Francois Petitjean, Germain Forestier, Anthony Bagnall, Eamonn Keogh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data
Subtitle of host publicationDec 11- 14, 2017 • Boston, MA, USA
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538627143, 9781538627150
ISBN (Print)9781538627167
Publication statusPublished - 2017
EventIEEE International Conference on Big Data (Big Data) 2017 - Boston, United States of America
Duration: 11 Dec 201714 Dec 2017
https://ieeexplore.ieee.org/xpl/conhome/8241556/proceeding (Proceedings)


ConferenceIEEE International Conference on Big Data (Big Data) 2017
Abbreviated titleIEEE BigData 2017
CountryUnited States of America
Internet address


  • classification
  • Dynamic Time Warping
  • time series

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