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
Implementation of "Dynamic principal components of periodically correlated functional time series".
Two examples in
library("pcdpca") demo("simulation") demo("pcdpca.pm10")
X be a multivariate time series, a matrix with
n observations and
d covariates, periodic with
period = 2. Then
FF = pcdpca(X, period=2) # finds the optimal filter Yhat = pcdpca.scores(X, FF) # applies the filter Yhat[,-1] = 0 # forces the use of only one component Xhat = pcdpca.inverse(Yhat, FF) # deconvolution cat(sum((X-Xhat)^2) / sum(X^2)) # variance explained