Spectrum-Based Comparison of Stationary Multivariate Time Series

Nalini Ravishanker (), J. R. M. Hosking and Jaydip Mukhopadhyay
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Nalini Ravishanker: University of Connecticut
J. R. M. Hosking: IBM Research Division
Jaydip Mukhopadhyay: Bristol Myers Squibb

Methodology and Computing in Applied Probability, 2010, vol. 12, issue 4, 749-762

Abstract: Abstract The problem of comparison of several multivariate time series via their spectral properties is discussed. A pairwise comparison between two independent multivariate stationary time series via a likelihood ratio test based on the estimated cross-spectra of the series yields a quasi-distance between the series. A hierarchical clustering algorithm is then employed to compare several time series given the quasi-distance matrix. For use in situations where components of the multivariate time series are measured in different units of scale, a modified quasi-distance based on a profile likelihood based estimation of the scale parameter is described. The approach is illustrated using simulated data and data on daily temperatures and precipitations at multiple locations. A comparison between hierarchical clustering based on the likelihood ratio test quasi-distance and a quasi-distance described in Kakizawa et al. (J Am Stat Assoc 93:328–340, 1998) is interesting.

Keywords: Hierarchical clustering; Likelihood ratio; Periodogram matrix; Quasi-distance; Spectral density matrix; 37M10; 62M15; 62H30 (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/s11009-010-9180-0

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