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: |
0.2.7 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
astsa (≥ 1.10), MASS, graphics, reshape2 (≥ 1.4.4), utils |
| Suggests: |
psd, fBasics, knitr, markdown, mgcv, nlme (≥ 3.1-64), testthat (≥ 2.0.0), rSFA |
| Published: |
2020-06-29 |
| DOI: |
10.32614/CRAN.package.ForeCA |
| Author: |
Georg M. Goerg [aut, cre] |
| Maintainer: |
Georg M. Goerg <im at gmge.org> |
| License: |
GPL-2 |
| URL: |
https://github.com/gmgeorg/ForeCA |
| NeedsCompilation: |
no |
| Citation: |
ForeCA citation info |
| Materials: |
README NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
ForeCA results |
Documentation:
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