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: 0.2.2
Depends: R (≥ 3.0.0), ifultools (≥ 2.0.0)
Imports: MASS, sapa
Suggests: astsa, mgcv, nlme (≥ 3.1-64), testthat (≥ 0.9.0), rSFA
Published: 2015-04-29
Author: Georg M. Goerg
Maintainer: Georg M. Goerg <im at gmge.org>
License: GPL-2
URL: http://www.gmge.org
NeedsCompilation: no
Citation: ForeCA citation info
Materials: NEWS
In views: TimeSeries
CRAN checks: ForeCA results

Downloads:

Reference manual: ForeCA.pdf
Package source: ForeCA_0.2.2.tar.gz
Windows binaries: r-devel: ForeCA_0.2.2.zip, r-release: ForeCA_0.2.2.zip, r-oldrel: ForeCA_0.2.2.zip
OS X Snow Leopard binaries: r-release: ForeCA_0.2.2.tgz, r-oldrel: ForeCA_0.2.2.tgz
OS X Mavericks binaries: r-release: ForeCA_0.2.2.tgz
Old sources: ForeCA archive