Refereed journal article or data article (A1)

Dimension Reduction for Time Series in a Blind Source Separation Context Using R

List of AuthorsNordhausen Klaus, Matilainen Markus, Miettinen Jari, Virta Joni, Taskinen Sara

PublisherAmerican Statistical Association

Publication year2021

JournalJournal of Statistical Software

Volume number98




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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.

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Last updated on 2022-19-12 at 15:57