Book Section #65311

Dau, Hoang Anh, Silva, Diego Furtado, Petitjean, François, Forestier, Germain, Bagnall, Anthony and Keogh, Eamonn (2017) UNSPECIFIED In: UNSPECIFIED IEEE Conference Publications, pp. 917-922.

[img] PDF (Accepted manuscript) - Submitted Version
Restricted to Repository staff only until 31 December 2099.

Download (824kB) | Request a copy


    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
    ?? RGMLS ??
    Related URLs:
    Depositing User: Pure Connector
    Date Deposited: 31 Oct 2017 06:10
    Last Modified: 17 Aug 2018 20:30
    DOI: 10.1109/BigData.2017.8258009

    Actions (login required)

    View Item