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Pattern Extraction for Time Series Classification

  • Pierre Geurts
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2168)

Abstract

In this paper, we propose some new tools to allow machine learning classifiers to cope with time series data. We first argue that many time-series classification problems can be solved by detecting and combining local properties or patterns in time series. Then, a technique is proposed to find patterns which are useful for classification. These patterns are combined to build interpretable classification rules. Experiments, carried out on several artificial and real problems, highlight the interest of the approach both in terms of interpretability and accuracy of the induced classifiers.

Keywords

Decision Tree Control Chart Dynamic Time Warping Pattern Extraction Candidate Test 
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 2001

Authors and Affiliations

  • Pierre Geurts
    • 1
  1. 1.Department of Electrical and Computer Engineering Institut MontefioreUniversity of LiègeLiègeBelgium

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