Forecastable Component Analysis (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 is
based on negative Shannon entropy of the spectral density of the
transformed signal. This R package provides the main algorithms and
auxiliary function(summary, plotting, etc) to apply ForeCA to multivariate
data (time series).

Forecastable Component Analysis (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. This R package provides the
main algorithms and auxiliary function (summary, plotting, etc.) to apply
ForeCA to multivariate time series data.
astsa, mgcv, nlme (>= 3.1-64), testthat (>= 0.9.0), rSFA,

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.

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.