Package 'pcdpca'

Compute periodically correlacted DPCA filter coefficients

Description

For a given periodically correlated multivariate process X eigendecompose it's spectral density and use an inverse fourier transform to get coefficients of the optimal filters.

Usage

pcdpca(X, period = NULL, q = 30, freq = (-1000:1000/1000) * pi)

Arguments

X	multivariate stationary time series
period	period of the periodic time series
q	window for spectral density estimation as in spectral.density
freq	frequency grid to estimate on as in spectral.density

Value

principal components series

References

Kidzinski, Kokoszka, Jouzdani Dynamic principal components of periodically correlated functional time series Research report, 2016

See Also

pcdpca.inverse, pcdpca.scores

Examples

## Prepare some process
library(fda)
library(freqdom)

MSE = function(X,Y=0){ sum((X-Y)**2) / nrow(X) }

d = 7
n = 100
A = t(t(matrix(rnorm(d*n),ncol=d,nrow=n))*7:1)
B = t(t(matrix(rnorm(d*n),ncol=d,nrow=n))*7:1)
C = t(t(matrix(rnorm(d*n),ncol=d,nrow=n))*7:1)

X = matrix(0,ncol=d,nrow=3*n)
X[3*(1:n) - 1,] = A
X[3*(1:n) - 2,] = A + B
X[3*(1:n) ,] = 2*A - B + C

basis = create.fourier.basis(nbasis=7)
X.fd = fd(t(Re(X)),basis=basis)
plot(X.fd)

## Hold out some datapoints
train = 1:(50*3)
test = (50*3) : (3*n)

## Static PCA ##
PR = prcomp(as.matrix(X[train,]))
Y1 = as.matrix(X) %*% PR$rotation
Y1[,-1] = 0
Xpca.est = Y1 %*% t(PR$rotation)

## Dynamic PCA ##
XI.est = dpca(as.matrix(X[train,]),
   q=3,
   freq=pi*(-150:150/150),
   Ndpc=1)  # finds the optimal filter
Y.est = freqdom::filter.process(X, XI.est$filters )
Xdpca.est = freqdom::filter.process(Y.est, t(rev(XI.est$filters)) )    # deconvolution

## Periodically correlated PCA ##
XI.est.pc = pcdpca(as.matrix(X[train,]),
   q=3,
   freq=pi*(-150:150/150),period=3)  # finds the optimal filter
Y.est.pc = pcdpca.scores(X, XI.est.pc)  # applies the filter
Y.est.pc[,-1] = 0 # forces the use of only one component
Xpcdpca.est = pcdpca.inverse(Y.est.pc, XI.est.pc)  # deconvolution

## Results
cat("NMSE PCA = ")
r0 = MSE(X[test,],Xpca.est[test,]) / MSE(X[test,],0)
cat(r0)
cat("\nNMSE DPCA = ")
r1 = MSE(X[test,],Xdpca.est[test,]) / MSE(X[test,],0)
cat(r1)
cat("\nNMSE PCDPCA = ")
r2 = MSE(X[test,],Xpcdpca.est[test,]) / MSE(X[test,],0)
cat(r2)
cat("\n")

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Retrieve a process from given scores

Description

For given scores Y and dynamic principal components XI retrive a series from which scores Y were calculated. This procedure should be seen as the inverse of pcdpca.scores.

Usage

pcdpca.inverse(Y, XI)

Arguments

Y	scores process
XI	principal components series

Value

Retrived process X

References

Kidzinski, Kokoszka, Jouzdani Dynamic principal components of periodically correlated functional time series Research report, 2016

See Also

pcdpca.scores, pcdpca

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Compute periodically correlated DPCA scores, given the filters XI

Description

Compute periodically correlated DPCA scores, given the filters XI

Usage

pcdpca.scores(X, XI)

Arguments

X	multivariate time series
XI	series of filters returned from pcdpca

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