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ForeCA  

Forecastable Component Analysis
View on CRAN: Click here


Download and install ForeCA package within the R console
Install from CRAN:
install.packages("ForeCA")

Install from Github:
library("remotes")
install_github("cran/ForeCA")

Install by package version:
library("remotes")
install_version("ForeCA", "0.2.7")



Attach the package and use:
library("ForeCA")
Maintained by
Georg M. Goerg
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-05-21
Latest Update: 2020-06-29
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.
How to cite:
Georg M. Goerg (2012). ForeCA: Forecastable Component Analysis. R package version 0.2.7, https://cran.r-project.org/web/packages/ForeCA. Accessed 25 Aug. 2026.
Previous versions and publish date:
0.0.1 (2012-05-21 14:08), 0.0.8.1 (2012-12-11 18:02), 0.0.9 (2012-12-12 21:52), 0.1 (2014-03-03 00:52), 0.2.0 (2015-02-20 11:58), 0.2.2 (2015-04-29 16:23), 0.2.4 (2016-03-30 08:14), 0.2.6 (2020-05-11 19:30), 0.2.7 (2020-06-29 14:40), (2026-08-23 07:00)
Other packages that cited ForeCA R package
View ForeCA citation profile
Other R packages that ForeCA depends, imports, suggests or enhances
Complete documentation for ForeCA
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