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Enhanced Weighted Dynamic Time Warping for Time Series Classification

  • Pichamon AnantasechEmail author
  • Chotirat Ann Ratanamahatana
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)

Abstract

Dynamic time warping (DTW) has been widely used as a distance measure for time series classification because its matching is elastic and robust in most cases. However, DTW may lead to over compression that could align too many consecutive points from one time series to only one point on another. As a result, important feature information could be overlooked, which can be the cause of misclassification particularly when the shape of time series is an essential feature. In order to fix this problem and improve the classification accuracy, we propose a distance measure called an enhanced weighted dynamic time warping, where weight functions are proposed and applied to the DTW distance measure. Other than being parameter-free, our experiment results have demonstrated to impressively outperform other rival methods by a large margin while having less time complexity than the state-of-the-art approaches.

Keywords

Dynamic time warping Weighted dynamic time warping Adaptive weight Time series classification 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Pichamon Anantasech
    • 1
    Email author
  • Chotirat Ann Ratanamahatana
    • 1
  1. 1.Department of Computer EngineeringChulalongkorn UniversityBangkokThailand

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