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freqdom (version 1.0.4)

Frequency Domain Analysis for Multivariate Time Series

Description

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.

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Version

Install

install.packages('freqdom')

Monthly Downloads

401

Version

1.0.4

License

GPL-3

Maintainer

Kidzinski L

Last Published

September 6th, 2015

Functions in freqdom (1.0.4)

Compute DPCA filter coefficients

Estimate regresson operators in a lagged linear model

Eigendevompose a frequency domain operator at each frequency

Compute operator norms of elements of a filter

Inverse Fourier transform for operators

Compute scores of dynamic principal components

Retrieve a process from given scores

Generate a linear process from

Estimate the optimal dimension in linear regression problem

Convolution of a process X with an operator A.

Estimate the optimal dimension in linear regression problem

Compute a ratio of two spectral densities

reglag.significance

Test significance of coefficients in linear model estimator

Frequency-wise or component-wise Kronecker product.

fourier.transform

Compute fourier transform of given series of operators

Generate brownian bridges

spectral.density

Compute the cross spectral density of processes X and Y

Compute a spectral norm of given matrix P

Moving avarege process

Plot a frequency domain operator

Truncates a time domain object to specified lags

Compute a mean square error between X and Y

Compute cross covariance with a given lag

Determine the subspace dimension for inversion in speclagreg

freqdom.transpose

Transpose pointwise timedom or freqdom object

Frequency-wise or component-wise matrix product.

Create a frequency domain operator

Check if a given object is a frequency domain matrix

Simulate a multivariate autoregressive time series

freqdom.kronecker

Compute a kronecker product of two spectral densities

Estimate the optimal dimension in linear regression problem

Generate a linear process from

Estimate the covariance structure within a given window \(k \in [-q,q]\)

freqdom.product

Compute a product of two spectral densities

Creates a time domain operator

Invert first K eigendirections of the matrix.

freqdom.inverse

Compute an inverse of a given Frequency Domain Operator

Check if a given object is a time domain object

Generate brownian motions