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

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

blind source separation, supervised dimension reduction, R.

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.

Klaus Nordhausen

https://www.jstatsoft.org/article/view/v098i15

Vienna University of Technology

May 2021

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