Dimension Reduction for Clustering Time Series Using Global Characteristics

  • Xiaozhe Wang
  • Kate A. Smith
  • Rob J. Hyndman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3516)


Existing methods for time series clustering rely on the actual data values can become impractical since the methods do not easily handle dataset with high dimensionality, missing value, or different lengths. In this paper, a dimension reduction method is proposed that replaces the raw data with some global measures of time series characteristics. These measures are then clustered using a self-organizing map. The proposed approach has been tested using benchmark time series previously reported for time series clustering, and is shown to yield useful and robust clustering.


Time Series Lyapunov Exponent Dimension Reduction Method Cluster Time Series Nonlinear Time Series Model 
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 2005

Authors and Affiliations

  • Xiaozhe Wang
    • 1
  • Kate A. Smith
    • 1
  • Rob J. Hyndman
    • 2
  1. 1.Faculty of Information TechnologyMonash UniversityClaytonAustralia
  2. 2.Department of Econometrics and Business StatisticsMonash UniversityClaytonAustralia

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