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
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