Discovering Patterns in Real-Valued Time Series

  • Joe Catalano
  • Tom Armstrong
  • Tim Oates
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


This paper describes an algorithm for discovering variable length patterns in real-valued time series. In contrast to most existing pattern discovery algorithms, ours does not first discretize the data, runs in linear time, and requires constant memory. These properties are obtained by sampling the data stream rather than processing all of the data. Empirical results show that the algorithm performs well on both synthetic and real data when compared to an exhaustive algorithm.


Dynamic Time Warping Sampling Algorithm Pattern Discovery Pattern Instance Dynamic Time Warping Distance 
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 2006

Authors and Affiliations

  • Joe Catalano
    • 1
  • Tom Armstrong
    • 1
  • Tim Oates
    • 1
  1. 1.University of Maryland Baltimore CountyBaltimoreUSA

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