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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.
Getting started
Browse package contents
Package details |
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| Author | Georg M. Goerg [aut, cre] |
| Maintainer | Georg M. Goerg <im@gmge.org> |
| License | GPL-2 |
| Version | 0.2.7 |
| URL | https://github.com/gmgeorg/ForeCA |
| Package repository | View on CRAN |
| Installation |
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