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.

Documentation

Manual: ForeCA.pdf
Vignette: None available.

Maintainer: Georg M. Goerg <im at gmge.org>

Author(s): Georg M. Goerg <im at gmge.org>

Install package and any missing dependencies by running this line in your R console:

install.packages("ForeCA")

Depends R (>= 3.0.0), ifultools(>=2.0.0)
Imports MASS, sapa, graphics, reshape2, utils
Suggests astsa, mgcv, nlme(>=3.1-64), testthat(>=0.9.0), rSFA,
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Package ForeCA
Materials
URL http://www.gmge.org
Task Views TimeSeries
Version 0.2.4
Published 2016-03-30
License GPL-2
BugReports
SystemRequirements
NeedsCompilation no
Citation
CRAN checks ForeCA check results
Package source ForeCA_0.2.4.tar.gz