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# pcdpca Implementation of "Dynamic principal components of periodically correlated functional time series". Two examples in `demo` directory: - pm10 data from Graz (comparizon with DFPCA paper) - simplation with parametrized periodicity ## Installation library("devtools") install_github("kidzik/pcdpca") ## Running a demo library("pcdpca") demo("simulation") demo("pcdpca.pm10") ## Usage Let `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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