Clusters of Multivariate Stationary Time Series by Differential Evolution and Autoregressive Distance

  • Roberto Baragona
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

Clustering MTS is a difficult task that has to be performed in several application fields. We propose a method based on the coefficients of vector autoregressive (VAR) models and differential evolution (DE) that may be applied to sets of stationary MTS. Results from a simulation experiment that includes both linear and non linear MTS are displayed for comparison with genetic algorithms (GAs), principal component analysis (PCA) and the k-means algorithm. Part of the Australian Sign Language (Auslan) data are examined to show the comparative performance of our procedure on a real world data set.

Keywords

Autoregressive distance Cluster analysis Differential evolution Multivariate time series 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Roberto Baragona
    • 1
  1. 1.Dept. of Communication and Social ResearchSapienza University of RomeRomeItaly

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