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Localized Random Shapelets

  • Mael GuilleméEmail author
  • Simon Malinowski
  • Romain Tavenard
  • Xavier Renard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11986)

Abstract

Shapelet models have attracted a lot of attention from researchers in the time series community, due in particular to its good classification performance. However, such models only inform about the presence/absence of local temporal patterns. Structural information about the localization of these patterns is ignored. In addition, end-to-end learning shapelet models tend to generate meaningless shapelets, leading to poorly interpretable models. In this paper, we aim at designing an interpretable shapelet model that takes into account the localization of the shapelets in the time series. Time series are transformed into feature vectors composed of both a distance and a localization information. Then, we design a hierarchical feature selection process using regularization. This process can be tuned to select, for each shapelet, either only its distance information or both distance and localization information. It is hence possible for every selected shapelet to analyze whether only the presence or the presence and the localization contributed to the decision process improving interpretability of the decision. Experiments show that this feature selection process has competitive performance compared to state-of-the-art shapelet-based classifiers, while providing better interpretability.

Keywords

Time series Machine learning Shapelets 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mael Guillemé
    • 1
    • 2
    Email author
  • Simon Malinowski
    • 2
  • Romain Tavenard
    • 3
  • Xavier Renard
    • 4
  1. 1.EnergiencyRennesFrance
  2. 2.Univ Rennes, Inria, CNRS, IRISARennesFrance
  3. 3.Univ Rennes, CNRS, LETG, IRISARennesFrance
  4. 4.AXAParisFrance

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