Clusters of multivariate stationary time series by differential evolution and autoregressive distance

Authors:
Roberto Baragona
Affiliations:
Sapienza University of Rome, Dept. of Communication and Social Research, Rome, Italy
Venue:
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Year:
2011

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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.