University of Oulu

Launonen, I. & Holmström, L. Stat Methods Appl (2017) 26: 361.

Multivariate posterior singular spectrum analysis

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Author: Launonen, Ilkka1; Holmström, Lasse1
Organizations: 1Department of Mathematical Sciences, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
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Language: English
Published: Springer Nature, 2017
Publish Date: 2017-10-27


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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Series: Statistical methods and applications
ISSN: 1618-2510
ISSN-E: 1613-981X
ISSN-L: 1618-2510
Volume: 26
Issue: 3
Pages: 361 - 382
DOI: 10.1007/s10260-016-0372-9
Type of Publication: A1 Journal article – refereed
Field of Science: 111 Mathematics
112 Statistics and probability
Funding: Research supported by Academy of Finland Project No. 250862 and a grant from the Alfred Kordelin Foundation.
Academy of Finland Grant Number: 250862
Detailed Information: 250862 (Academy of Finland Funding decision)
Copyright information: © Springer-Verlag Berlin Heidelberg 2016. This is a post-peer-review, pre-copyedit version of an article published in Stat Methods Appl. The final authenticated version is available online at: