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 and supervised dimension reduction problem for multivariate time series. Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace. The package is fully described in Nordhausen, Matilainen, Miettinen, Virta and Taskinen (2021) .


Reference manual

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1.0.0 by Markus Matilainen, 7 months ago

Browse source code at

Authors: Markus Matilainen [cre, aut] , Christophe Croux [aut] , Jari Miettinen [aut] , Klaus Nordhausen [aut] , Hannu Oja [aut] , Sara Taskinen [aut] , Joni Virta [aut]

Documentation:   PDF Manual  

Task views: Time Series Analysis

GPL (>= 2) license

Imports Rcpp, forecast, boot, parallel, xts, zoo

Depends on ICtest, JADE, BSSprep

Suggests stochvol, MTS, tsbox, dr

Linking to Rcpp, RcppArmadillo

Imported by tensorBSS.

Depended on by ssaBSS.

See at CRAN