pcdpca: Dynamic Principal Components for Periodically Correlated
Functional Time Series
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>.
| Version: |
0.4 |
| Depends: |
R (≥ 3.3.1) |
| Imports: |
freqdom, fda |
| Published: |
2017-09-03 |
| Author: |
Lukasz Kidzinski [aut, cre],
Neda Jouzdani [aut],
Piotr Kokoszka [aut] |
| Maintainer: |
Lukasz Kidzinski <lukasz.kidzinski at stanford.edu> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
README |
| In views: |
FunctionalData, TimeSeries |
| CRAN checks: |
pcdpca results |
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