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 |
Downloads:
Reverse dependencies:
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