TSA (Time Series Analysis) Toolbox 4.1
=======================================

The TSA toolbox is useful for analysing Time Series. 
The methods are based on stochastic concepts and 
maximum entropy methods. The programs should be compatible 
to Matlab as well as Octave. 

It includes
- Stochastic Signal processing 
- Autoregressive Model Identification 
- adaptive autoregressive modelling using Kalman filtering
- multivariate autoregressive modelling 
- maximum entropy spectral estimation 
- matched (inverse) filter design
- Histogram analysis  
- Calcution of the entropy of a time series
- Non-linear analysis (3rd order statistics) 
- Test for UnitCircle- and Hurwitz- Polynomials
- multiple signal processing 
- Several criteria (AIC, BIC, FPE, MDL, SBC, CAT, PHI) for 
model order selection  an autoregressive model are included.
- Fast algorithms are used 
- missing values (encoded as NaN's) are considered

Furthermore includes the toolbox a fast version of the Yule-Walker method 
for estimating Autoregressive parameters, Autocorrelation Function (ACF), 
Partial autocorrelation (PARCOR), reflection coefficients and some 
other useful staff. Demo programs can be started with "demo" or "demotsa". 


% This library is free software; you can redistribute it and/or
% modify it under the terms of the GNU Library General Public
% License as published by the Free Software Foundation; either
% Version 3 of the License, or (at your option) any later version.
%
% This library is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
% Library General Public License for more details.
%
% You should have received a copy of the GNU Library General Public
% License along with this library; if not, see
% <http://www.gnu.org/licenses/>.

Copyright (C) 1996-2003,2010,2012 by Alois Schloegl 
E-Mail:   alois.schloegl@ist.ac.at
WWW:      http://pub.ist.ac.at/~schloegl/matlab/tsa/