tsBSS: Blind Source Separation and Supervised Dimension Reduction for Time Series

Different estimators are provided to solve the blind source separation problem for multivariate time series with stochastic volatility (Matilainen, Nordhausen and Oja (2015) <doi:10.1016/j.spl.2015.04.033>; Matilainen, Miettinen, Nordhausen, Oja and Taskinen (2017) <doi:10.17713/ajs.v46i3-4.671>) and supervised dimension reduction problem for multivariate time series (Matilainen, Croux, Nordhausen and Oja (2017) <doi:10.1016/j.ecosta.2017.04.002>). Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace.

Version: 0.5.5
Depends: ICtest (≥ 0.3-2), JADE (≥ 2.0-2)
Imports: Rcpp (≥ 0.11.0), forecast, boot, parallel, xts, zoo
LinkingTo: Rcpp, RcppArmadillo
Suggests: stochvol
Published: 2019-10-14
Author: Markus Matilainen, Christophe Croux, Jari Miettinen, Klaus Nordhausen, Hannu Oja, Sara Taskinen, Joni Virta
Maintainer: Markus Matilainen <markus.matilainen at outlook.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: ChangeLog
CRAN checks: tsBSS results


Reference manual: tsBSS.pdf
Package source: tsBSS_0.5.5.tar.gz
Windows binaries: r-devel: tsBSS_0.5.5.zip, r-release: tsBSS_0.5.5.zip, r-oldrel: tsBSS_0.5.5.zip
macOS binaries: r-release: tsBSS_0.5.5.tgz, r-oldrel: tsBSS_0.5.5.tgz
Old sources: tsBSS archive

Reverse dependencies:

Reverse imports: tensorBSS


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