ForeCA is a novel dimension reduction (DR) technique for temporally dependent signals. Contrary to other popular DR methods, such as PCA or ICA, ForeCA explicitly searches for the most ”forecastable” signal. The measure of forecastability Omega(x_t) is based on negative Shannon entropy of the spectral density of the transformed signal. This R package provides the main algorithms and some auxiliary function (summary, plotting, etc).
| Version: | 0.0.9 |
| Depends: | R (≥ 2.12.0), sapa, mgcv, astsa, class |
| Imports: | mgcv, sapa, R.utils |
| Published: | 2012-12-12 |
| Author: | Georg M. Goerg |
| Maintainer: | Georg M. Goerg <gmg at stat.cmu.edu> |
| License: | GPL (≥ 2) |
| URL: | http://www.stat.cmu.edu/~gmg http://arxiv.org/abs/1205.4591 |
| NeedsCompilation: | no |
| In views: | TimeSeries |
| CRAN checks: | ForeCA results |
| Package source: | ForeCA_0.0.9.tar.gz |
| MacOS X binary: | ForeCA_0.0.9.tgz |
| Windows binary: | ForeCA_0.0.9.zip |
| Reference manual: | ForeCA.pdf |
| News/ChangeLog: | NEWS |
| Old sources: | ForeCA archive |