Discrimination and clustering for multivariate time series

Yoshihide Kakizawa*, Robert H. Shumway, Masanobu Taniguchi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

218 Citations (Scopus)


Minimum discrimination information provides a useful generalization of likelihood methodology for classification and clustering of multivariate time series. Discrimination between different classes of multivariate time series that can be characterized by differing covariance or spectral structures is of importance in applications occurring in the analysis of geophysical and medical time series data. For discrimination between such multivariate series, Kullback-Leibler discrimination information and the Chernoff information measure are developed for the multivariate non-Gaussian case. Asymptotic error rates and limiting distributions are given for a generalized spectral disparity measure that includes the foregoing criteria as special cases. Applications to problems of clustering and classifying earthquakes and mining explosions are given.

Original languageEnglish
Pages (from-to)328-340
Number of pages13
JournalJournal of the American Statistical Association
Issue number441
Publication statusPublished - 1998 Mar 1
Externally publishedYes


  • Chernoff
  • Divergence
  • Kullback-Leibler
  • Minimum discrimination information
  • Robustness
  • Seismology
  • Spectral analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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