ForeCA: Forecastable Component Analysis

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.

Version:	0.2.7
Depends:	R (≥ 3.5.0)
Imports:	astsa (≥ 1.10), MASS, graphics, reshape2 (≥ 1.4.4), utils
Suggests:	psd, fBasics, knitr, markdown, mgcv, nlme (≥ 3.1-64), testthat (≥ 2.0.0), rSFA
Published:	2020-06-29
DOI:	10.32614/CRAN.package.ForeCA
Author:	Georg M. Goerg [aut, cre]
Maintainer:	Georg M. Goerg <im at gmge.org>
License:	GPL-2
URL:	https://github.com/gmgeorg/ForeCA
NeedsCompilation:	no
Citation:	ForeCA citation info
Materials:	README NEWS
In views:	TimeSeries
CRAN checks:	ForeCA results

Documentation:

Reference manual:	ForeCA.pdf
Vignettes:	An Introduction to the ForeCA R package

Downloads:

Package source:	ForeCA_0.2.7.tar.gz
Windows binaries:	r-devel: ForeCA_0.2.7.zip, r-release: ForeCA_0.2.7.zip, r-oldrel: ForeCA_0.2.7.zip
macOS binaries:	r-release (arm64): ForeCA_0.2.7.tgz, r-oldrel (arm64): ForeCA_0.2.7.tgz, r-release (x86_64): ForeCA_0.2.7.tgz, r-oldrel (x86_64): ForeCA_0.2.7.tgz
Old sources:	ForeCA archive

Linking:

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