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.
Package details 


Author  Georg M. Goerg <im@gmge.org> 
Date of publication  20160330 08:14:22 
Maintainer  Georg M. Goerg <im@gmge.org> 
License  GPL2 
Version  0.2.4 
URL  http://www.gmge.org 
Package repository  View on CRAN 
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