%Vector time series analysis
%===========================
%
% Package for time series analysis of vector signals
% using autoregressive models. The autoregressive model
% is given by:
% a0*x(n)+a1*x(n-1)+ ... +ap*x(n-p) = e(n),
% where e(n) is a white noise signal.
%
%Notation
% Suppose the given signal is an 3-d signal x(n), n = 1,...,100.
% These signals are then combined in a 3x1x100 matrix, with:
% x(:,:,1) = x(1);
% ...
% x(:,:,100) = x(100),
% For a 3-D signal, x(1),...,x(100) are 3x1 matrices.
%
% Similarly, the parameter matrices in the autoregressive difference equation are
% given by an array of 3x3 matrices a0 up to ap:
% a(:,:,1) = a0;
% ...
% a(:,:,p) = ap;
%
%Estimation
% arselv - Parameter estimation and order selection for vector AR models
% burgv - The Burg estimator for vectors (Nuttall-Strand)
% moderrarv - Vector Model Error for AR models
% kullbleibv - Kullback-Leibler discrepancy
%
%Parameter conversion
% pc2covv, cov2pcv - partial correlations <--> covariance function
% par2covv, cov2parv - parameters <--> covariance function
% pc2arset - partial correlations --> parameter set
% pc2rcv, rc2pcv - partial correlations <--> reflection coefficients
%
%Spectra
% pc2specv - Auto-and cross-spectra
% pc2xspecv - Cross-spectrum
% pc2cohv - Coherence function
%
%Data processing
% armafilterv - Digital ARMA filter for matrix-valued signals.
%
% Reference:
% S. de Waele,
% "Automatic model inference from finite time observations
% of stationary stochastic signals",
% Ph.D. Thesis, Delft university of Technology, 2003.
