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ForeCA (version 0.2.6)

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

334

Version

0.2.6

License

GPL-2

Maintainer

Georg M Goerg

Last Published

May 11th, 2020

Functions in ForeCA (0.2.6)

continuous_entropy

Shannon entropy for a continuous pdf
common-arguments

List of common arguments
foreca-utils

Plot, summary, and print methods for class 'foreca'
ForeCA-package

Implementation of Forecastable Component Analysis (ForeCA)
complete-controls

Completes several control settings
Omega

Estimate forecastability of a time series
foreca

Forecastable Component Analysis
foreca.EM-aux

ForeCA EM auxiliary functions
foreca.EM.one_weightvector

EM-like algorithm to estimate optimal ForeCA transformation
discrete_entropy

Shannon entropy for discrete pmf
sfa

Slow Feature Analysis
spectral_entropy

Estimates spectral entropy of a time series
mvspectrum2wcov

Compute (weighted) covariance matrix from frequency spectrum
mvspectrum

Estimates spectrum of multivariate time series
mvspectrum-utils

S3 methods for class 'mvspectrum'
quadratic_form

Computes quadratic form x' A x
whiten

whitens multivariate data
initialize_weightvector

Initialize weightvector for iterative ForeCA algorithms
foreca.one_weightvector-utils

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