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Learning Multiple Temporal Matching for Time Series Classification

  • Cedric Frambourg
  • Ahlame Douzal-Chouakria
  • Eric Gaussier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8207)

Abstract

In real applications, time series are generally of complex structure, exhibiting different global behaviors within classes. To discriminate such challenging time series, we propose a multiple temporal matching approach that reveals the commonly shared features within classes, and the most differential ones across classes. For this, we rely on a new framework based on the variance/covariance criterion to strengthen or weaken matched observations according to the induced variability within and between classes. The experiments performed on real and synthetic datasets demonstrate the ability of the multiple temporal matching approach to capture fine-grained distinctions between time series.

Keywords

Time Series Synthetic Dataset Dynamic Time Warping Discriminative Feature Multivariate Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Cedric Frambourg
    • 1
  • Ahlame Douzal-Chouakria
    • 1
  • Eric Gaussier
    • 1
  1. 1.Grenoble 1 / CNRS / LIGUniversité Joseph FourierFrance

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