Nothing
Implementation of "Dynamic principal components of periodically correlated functional time series".
Two examples in demo
directory:
library("devtools")
install_github("kidzik/pcdpca")
library("pcdpca")
demo("simulation")
demo("pcdpca.pm10")
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
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.