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.
|Author||Georg M. Goerg <email@example.com>|
|Date of publication||2016-03-30 08:14:22|
|Maintainer||Georg M. Goerg <firstname.lastname@example.org>|
common-arguments: List of common arguments
complete-controls: Completes several control settings
continuous_entropy: Shannon entropy for a continuous pdf
discrete_entropy: Shannon entropy for discrete pmf
foreca: Forecastable Component Analysis
foreca.EM-aux: ForeCA EM auxiliary functions
foreca.EM.one_weightvector: EM-like algorithm to estimate optimal ForeCA transformation
foreca.one_weightvector-utils: Plot, summary, and print methods for class...
ForeCA-package: Implementation of Forecastable Component Analysis (ForeCA)
foreca-utils: Plot, summary, and print methods for class 'foreca'
initialize_weightvector: Initialize weightvector for iterative ForeCA algorithms
mvspectrum: Estimates spectrum of multivariate time series
mvspectrum2wcov: Compute (weighted) covariance matrix from frequency spectrum
mvspectrum-utils: S3 methods for class 'mvspectrum'
Omega: Estimate forecastability of a time series
quadratic_form: Computes quadratic form x' A x
sfa: Slow Feature Analysis
spectral_entropy: Estimates spectral entropy of a time series
whiten: whitens multivariate data