dprcomp

multivariate stationary time series

correlation structure between coefficients of vectors (default diagonal)

lags

window for spectral density estimation as in <code><a rd-options="" href="/link/spectral.density?package=freqdom&version=1.0.4" data-mini-rdoc="freqdom::spectral.density">spectral.density</a></code>

as in <code><a rd-options="" href="/link/spectral.density?package=freqdom&version=1.0.4" data-mini-rdoc="freqdom::spectral.density">spectral.density</a></code>

weights

frequency grid to estimate on as in <code><a rd-options="" href="/link/spectral.density?package=freqdom&version=1.0.4" data-mini-rdoc="freqdom::spectral.density">spectral.density</a></code>

freq

For a given process <code>X</code> eigendecompose it's spectral density
and use an inverse fourier transform to get coefficients of the optimal
filter. For details please refer to Hormann et al paper.

Methods for the analysis of multivariate time series using frequency domain techniques. Implementations of dynamic principle components analysis (DPCA) and estimators of operators in lagged regression. Examples of usage in functional data analysis setup.

Kidzinski L

freqdom

Frequency Domain Analysis for Multivariate Time Series

dprcomp function

window for spectral density estimation as in <code><a rd-options='' href='spectral.density'>spectral.density</a></code>

as in <code><a rd-options='' href='spectral.density'>spectral.density</a></code>

frequency grid to estimate on as in <code><a rd-options='' href='spectral.density'>spectral.density</a></code>

dprcomp: Compute DPCA filter coefficients

Description

Usage

Arguments

Value

References

See Also