A2 Review article, Literature review, Systematic review
A review of second‐order blind identification methods (2022)

Pan, Y., Matilainen, M., Taskinen, S., & Nordhausen, K. (2022). A review of second‐order blind identification methods. WIREs Computational Statistics, 14(4), Article e1550. https://doi.org/10.1002/wics.1550

JYU authors or editors

Publication details

All authors or editors: Pan, Yan; Matilainen, Markus; Taskinen, Sara; Nordhausen, Klaus

Journal or series: WIREs Computational Statistics

ISSN: 1939-5108

eISSN: 1939-0068

Publication year: 2022

Publication date: 07/02/2021

Volume: 14

Issue number: 4

Article number: e1550

Publisher: John Wiley & Sons

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1002/wics.1550

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/78633


Second order source separation (SOS) is a data analysis tool which can be used for revealing hidden structures in multivariate time series data or as a tool for dimension reduction. Such methods are nowadays increasingly important as more and more high-dimensional multivariate time series data are measured in numerous fields of applied science. Dimension reduction is crucial, as modelling such high-dimensional data with multivariate time series models is often impractical as the number of parameters describing dependencies between the component time series is usually too high. SOS methods have their roots in the signal processing literature, where they were first used to separate source signals from an observed signal mixture. The SOS model assumes that the observed time series (signals) is a linear mixture of latent time series (sources) with uncorrelated components. The methods make use of the second order statistics - hence the name “second order source separation”. In this review we discuss the classical SOS methods and their extensions to more complex settings. An example illustrates how SOS can be performed.

Keywords: statistical methods; multivariable methods; time series; time-series analysis; signal processing; computational science

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

Preliminary JUFO rating: 1

Last updated on 2022-20-09 at 14:38