A1 Refereed original research article in a scientific journal
Dimension Reduction for Time Series in a Blind Source Separation Context Using R
Authors: Nordhausen Klaus, Matilainen Markus, Miettinen Jari, Virta Joni, Taskinen Sara
Publisher: American Statistical Association
Publication year: 2021
Journal: Journal of Statistical Software
Article number: 15
Volume: 98
eISSN: 1548-7660
DOI: https://doi.org/10.18637/jss.v098.i15(external)
Web address : https://www.jstatsoft.org/article/view/v098i15(external)
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/44561561(external)
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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