TSA (Time Series Analysis) Toolbox 2.40
=======================================

The TSA toolbox is useful for analysing (univariate, stationary) 
Time Series. In comparison to other signal processing tools, the
methods used here, are based on maximum entropy methods.
It can be used for
- Stochastic Signal processing 
- Autoregressive Model Identification 
- matched (inverse) filter design
- Histogram analysis <NEW>  
- Calcution of the entropy of a time series
- Non-linear analysis (3rd order statistics) 
- Time-varying and Non-stationary Analysis <in preparation>
	Recursive Least squares (RLS) algorithm
	Least Mean Square (LMS) algorithm
- Test for UnitCircle-Polynomials <NEW>
- Test for Hurwitz-Polynomials <NEW>
- multiple signal processing 

Several criteria (AIC, BIC, FPE, MDL, SBC, CAT, PHI) for 
the selection of the order of an autoregressive model are included.
Furthermore includes the toolbox a fast version if the Yule-Walker method 
for estimating Autoregressive parameters, the Autocovariance Function (ACovF), 
Autocorrelation Function (ACF), Partial ACF (PACF), and some 
other useful staff. Demo programs can be started with "demo" or "demotsa". 

Modifications of V2.40:
From Version 2.32 and higher many functions can be used for multiple 
channels. 
ACOVF, ACORF, UCP, HUP, durlev (instead of yuwa), earpyw, 


% 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 2 of the License, or (at your option) any later version.
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% 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.
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% You should have received a copy of the GNU Library General Public
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% Free Software Foundation, Inc., 59 Temple Place - Suite 330,
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Copyright (c) 1996-1998 by Alois SCHLOEGL
E-Mail:   a.schloegl@ieee.org
WWW:      http://www-dpmi.tu-graz.ac.at/~schloegl/matlab/tsa


Special thanks to the following people and organizations: 
---------------------------------------------------------
Keith Briggs	for finding errors and suggesting improvements
Philip Gray	for his suggestion to include stderr, p-values and Q-stats in ACORF
Prof. G. Pfurtscheller