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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.