A1 Journal article (refereed)
Dimension Reduction for Time Series in a Blind Source Separation Context Using R (2021)

Nordhausen, K., Matilainen, M., Miettinen, J., Virta, J., & Taskinen, S. (2021). Dimension Reduction for Time Series in a Blind Source Separation Context Using R. Journal of Statistical Software, 98, Article 15. https://doi.org/10.18637/jss.v098.i15

JYU authors or editors

Publication details

All authors or editors: Nordhausen, Klaus; Matilainen, Markus; Miettinen, Jari; Virta, Joni; Taskinen, Sara

Journal or series: Journal of Statistical Software

eISSN: 1548-7660

Publication year: 2021

Volume: 98

Article number: 15

Publisher: Foundation for Open Access Statistic

Publication country: United States

Publication language: English

DOI: https://doi.org/10.18637/jss.v098.i15

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/77132

Abstract

Multivariate time series observations are increasingly common in multiple fields of science but the complex dependencies of such data often translate into intractable models with large number of parameters. An alternative is given by first reducing the dimension of the series and then modelling the resulting uncorrelated signals univariately, avoiding the need for any covariance parameters. A popular and effective framework for this is blind source separation. In this paper we review the dimension reduction tools for time series available in the R package tsBSS. These include methods for estimating the signal dimension of second-order stationary time series, dimension reduction techniques for stochastic volatility models and supervised dimension reduction tools for time series regression. Several examples are provided to illustrate the functionality of the package.

Keywords: time-series analysis; multivariable methods; signal analysis; signal processing; R (programming languages)

Free keywords: blind source separation; supervised dimension reduction; R

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2021

JUFO rating: 1