TSA (Time Series Analysis) Toolbox 4.2
=======================================

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". 


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Copyright (C) 1996-2003,2010,2012,2014 by Alois Schloegl 
E-Mail:   alois.schloegl@ist.ac.at
WWW:      http://pub.ist.ac.at/~schloegl/matlab/tsa/


