ForeCA (0.2.6)

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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.

Maintainer: Georg M. Goerg
Author(s): Georg M. Goerg [aut, cre]

License: GPL-2

Uses: ifultools, MASS, reshape2, sapa, fBasics, mgcv, nlme, testthat, knitr, rSFA, astsa
Reverse suggests: feasts

Released 17 days ago.

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