https://github.com/cran/pcdpca
Tip revision: ce9d67e15a5402f4d12e62fe9882ada23b12971e authored by Lukasz Kidzinski on 26 November 2016, 23:06:38 UTC
version 0.2.1
version 0.2.1
Tip revision: ce9d67e
README.md
# 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