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Multivariate posterior singular spectrum analysis

Author

Listed:
  • Ilkka Launonen

    (University of Oulu)

  • Lasse Holmström

    (University of Oulu)

Abstract

A generalized, multivariate version of the Posterior Singular Spectrum Analysis (PSSA) method is described for the identification of credible features in multivariate time series. We combine Bayesian posterior modeling with multivariate SSA (MSSA) and infer the MSSA signal components with a credibility analysis of the posterior sample. The performance of multivariate PSSA (MPSSA) is compared to the single-variate PSSA with an artificial example and the potential of MPSSA is demonstrated with real data using NAO and SOI climate index series.

Suggested Citation

  • Ilkka Launonen & Lasse Holmström, 2017. "Multivariate posterior singular spectrum analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 361-382, August.
    • Handle: RePEc:spr:stmapp:v:26:y:2017:i:3:d:10.1007_s10260-016-0372-9
    DOI: 10.1007/s10260-016-0372-9
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    References listed on IDEAS

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    1. Golyandina, Nina & Korobeynikov, Anton & Shlemov, Alex & Usevich, Konstantin, 2015. "Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i02).
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