dpca.scores {freqdom} | R Documentation |
Obtain dynamic principal components scores
Description
Computes dynamic principal component score vectors of a vector time series.
Usage
dpca.scores(X, dpcs = dpca.filters(spectral.density(X)))
Arguments
X |
a vector time series given as a |
dpcs |
an object of class |
Details
The -th dynamic principal components score sequence is defined by
where are the dynamic PC filters as explained in
dpca.filters
. For the sample version the sum extends
over the range of lags for which the are defined. The actual operation carried out is
filter.process(X, A = dpcs)
.
We for more details we refer to Chapter 9 in Brillinger (2001), Chapter 7.8 in Shumway and Stoffer (2006) and to Hormann et al. (2015).
Value
A
Ndpc
-matix with Ndpc = dim(dpcs$operators)[1]
. The -th column contains the
-th dynamic principal component score sequence.
References
Hormann, S., Kidzinski, L., and Hallin, M. Dynamic functional principal components. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 77.2 (2015): 319-348.
Brillinger, D. Time Series (2001), SIAM, San Francisco.
Shumway, R.H., and Stoffer, D.S. Time Series Analysis and Its Applications (2006), Springer, New York.
See Also
dpca.filters
, dpca.KLexpansion
, dpca.var