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) ; Matilainen, Miettinen, Nordhausen, Oja and Taskinen (2017) ) and supervised dimension reduction problem for multivariate time series (Matilainen, Croux, Nordhausen and Oja (2017) ). Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace.


Reference manual

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0.5.7 by Markus Matilainen, 14 days 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  

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