Description: 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.

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
The measure of forecastability is based on the Shannon entropy of the spectral
Imports: MASS, sapa, graphics, reshape2, utils

Imports: MASS, sapa (>= 2.0.0), graphics, reshape2 (>= 1.4.4), utils
Suggests: astsa (>= 1.10), fBasics, knitr, mgcv, nlme (>= 3.1-64),

Imports: astsa (>= 1.10), MASS, graphics, reshape2 (>= 1.4.4), utils
Suggests: psd, fBasics, knitr, markdown, mgcv, nlme (>= 3.1-64),

Author: Christoph E. Weiss, Gernot R. Roetzer, Eran Raviv
Maintainer: Christoph E. Weiss <info@ceweiss.com>
Description: Provides geometric- and regression-based forecast
combination methods under a unified user interface for the packages 'ForecastCombinations'
and 'GeomComb'. Additionally, updated tools and convenience functions for data pre-processing are available in order to deal with
common problems in forecast combination (missingness, collinearity). For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>,
Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>,

Description: Aim: Supports the most frequently used methods to combine forecasts. Among others: Simple average, Ordinary Least Squares, Least Absolute Deviation, Constrained Least Squares, Variance-based, Best Individual model, Complete subset regressions and Information-theoretic (information criteria based).