pcdpca.inverse

<p>For given scores <code>Y</code> and dynamic principal components <code>XI</code>
retrive a series from which scores <code>Y</code> were calculated.
This procedure should be seen as the inverse of <code><a rd-options="" href="/link/pcdpca.scores?package=pcdpca&version=0.4" data-mini-rdoc="pcdpca::pcdpca.scores">pcdpca.scores</a></code>.</p>

Method extends multivariate and functional dynamic principal components
to periodically correlated multivariate time series. This package allows you to
compute true dynamic principal components in the presence of periodicity.
We follow implementation guidelines as described in Kidzinski, Kokoszka and
Jouzdani (2017), in Principal component analysis of periodically correlated
functional time series <arXiv:1612.00040>.

Lukasz Kidzinski

pcdpca

Dynamic Principal Components for Periodically Correlated
Functional Time Series

Neda Jouzdani

Piotr Kokoszka

pcdpca.inverse function

<p>For given scores <code>Y</code> and dynamic principal components <code>XI</code>
retrive a series from which scores <code>Y</code> were calculated.
This procedure should be seen as the inverse of <code><a rd-options='' href='pcdpca.scores'>pcdpca.scores</a></code>.</p>

pcdpca.inverse: Retrieve a process from given scores

Description

Usage

Arguments

Value

References

See Also