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Multivariate posterior singular spectrum analysis

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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.

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Authors and Affiliations

  1. Department of Mathematical Sciences, University of Oulu, Oulu, Finland

    Ilkka Launonen & Lasse Holmström

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  1. Ilkka Launonen
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  2. Lasse Holmström
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Correspondence to Ilkka Launonen.

Additional information

Ilkka Launonen: Research supported by Academy of Finland Project No. 250862 and a grant from the Alfred Kordelin Foundation.

Lasse Holmström: Research supported by Academy of Finland Project No. 250862.

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Launonen, I., Holmström, L. Multivariate posterior singular spectrum analysis. Stat Methods Appl 26, 361–382 (2017). https://doi.org/10.1007/s10260-016-0372-9

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  • DOI: https://doi.org/10.1007/s10260-016-0372-9

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