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ForeCA (version 0.2.4)

Forecastable Component Analysis

Description

Implementation of Forecastable Component Analysis ('ForeCA'), including main algorithms and auxiliary function (summary, plotting, etc.) to apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension reduction (DR) technique for temporally dependent signals. Contrary to other popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency explicitly into account and searches for the most ''forecastable'' signal. The measure of forecastability is based on the Shannon entropy of the spectral density of the transformed signal.

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Version

Install

install.packages('ForeCA')

Monthly Downloads

369

Version

0.2.4

License

GPL-2

Maintainer

Georg M Goerg

Last Published

March 30th, 2016

Functions in ForeCA (0.2.4)

initialize_weightvector

Initialize weightvector for iterative ForeCA algorithms
mvspectrum-utils

S3 methods for class 'mvspectrum'
foreca.EM-aux

ForeCA EM auxiliary functions
foreca.EM.one_weightvector

EM-like algorithm to estimate optimal ForeCA transformation
foreca

Forecastable Component Analysis
foreca.one_weightvector-utils

Plot, summary, and print methods for class 'foreca.one_weightvector'
quadratic_form

Computes quadratic form x' A x
sfa

Slow Feature Analysis
ForeCA-package

Implementation of Forecastable Component Analysis (ForeCA)
foreca-utils

Plot, summary, and print methods for class 'foreca'
common-arguments

List of common arguments
complete-controls

Completes several control settings
continuous_entropy

Shannon entropy for a continuous pdf
Omega

Estimate forecastability of a time series
spectral_entropy

Estimates spectral entropy of a time series
discrete_entropy

Shannon entropy for discrete pmf
mvspectrum2wcov

Compute (weighted) covariance matrix from frequency spectrum
mvspectrum

Estimates spectrum of multivariate time series
whiten

whitens multivariate data