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freqdom (version 2.0.2)

Frequency Domain Based Analysis: Dynamic PCA

Description

Implementation of dynamic principal component analysis (DPCA), simulation of VAR and VMA processes and frequency domain tools. These frequency domain methods for dimensionality reduction of multivariate time series were introduced by David Brillinger in his book Time Series (1974). We follow implementation guidelines as described in Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component .

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Version

Install

install.packages('freqdom')

Monthly Downloads

387

Version

2.0.2

License

GPL-3

Maintainer

Lukasz Kidzinski

Last Published

April 18th, 2022

Functions in freqdom (2.0.2)

dpca.KLexpansion

Dynamic KL expansion
dpca.scores

Obtain dynamic principal components scores
fourier.transform

Computes the Fourier transformation of a filter given as timedom object
filter.process

Convolute (filter) a multivariate time series using a time-domain filter
freqdom-package

Frequency domain basde analysis: dynamic PCA
rar

Simulate a multivariate autoregressive time series
is.timedom

Checks if an object belongs to the class timedom
dpca.var

Proportion of variance explained
+.freqdom

Frequency-wise sum of freqdom objects
fourier.inverse

Coefficients of a discrete Fourier transform
rev

Invert order of lags or grid parameters of a timedom or freqdom object, respectively
spectral.density

Compute empirical spectral density
%*%

Frequency-wise product of freqdom objects
summary.freqdom

Print object summary
is.freqdom

Checks if an object belongs to the class freqdom
print.timedom

Print timedom object
print.freqdom

Print freqdom object
rev.timedom

rev Reverts order of lags in an object of class timedom
dpca

Compute Dynamic Principal Components and dynamic Karhunen Loeve extepansion
dpca.filters

Compute DPCA filter coefficients
timedom.norms

Compute operator norms of elements of a filter
timedom.trunc

Choose lags of an object of class timedom
rma

Moving average process
cov.structure

Estimate cross-covariances of two stationary multivariate time series
freqdom

Create an object corresponding to a frequency domain functional
-.freqdom

Frequency-wise difference of freqdom objects
freqdom.eigen

Eigendecompose a frequency domain operator at each frequency
freqdom.product

Compute a matrix product of two frequency-domain operators
freqdom.transpose

Compute a transpose of a given frequency-domain operator at each frequency
timedom

Defines a linear filter
summary.timedom

Print object summary
+.timedom

Time-wise sum of freqdom objects
plus.freqdom

Frequency-wise sum of freqdom objects
-.timedom

Time-wise difference of freqdom objects