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The Wayback Machine - https://web.archive.org/web/20140414204329/http://pub.ist.ac.at/~schloegl/matlab/tsa/
Time
Series Analysis with Matlab�
Version 4.2.7
The TSA toolbox is useful for analyzing (uni- and multivariate, stationary and non-stationary) Time
Series.
An Introductory
tour to Time Series Analysis and the Download
site can be found here.
It can be used for:
1. stochastic signal processing
2. autoregressive model identification
3. matched (inverse) filter design
4. Histogram analysis
5. Calcution of the entropy of a timeseries
6. Non-linear analysis (3rd order statistics)
7. smoothing, prediction, filtering
8. Test for Hurwitz and Unit-Circle Polynomials
9. handles missing values (NaN's)
Several
criteria (AIC, BIC, FPE, MDL, SBC, CAT, PHI) for the selection of the order
of an autoregressivemodel are included. Furthermore includes the toolbox a fast
version ifthe Yule-Walker method for estimating Autoregressive parameters, the
AutocovarianceFunction (ACovF), Autocorrelation Function (ACF), Partial ACF
(PACF),andsome other useful staff. Demo programs can be started with "demo"
or "demotsa". Version 2.40 (and higher) provides fast algorithms for
testing polynomials; and many functions (e.g. ACovF and the Levinson-Durbin
algorithms) are implemented for multiple series.
Latest
Version 4.2.7, 13 Jan 2014. Tested with Matlab 7.x and Octave 3.x
refer
to:
Schl�gl, A.; "Time Series Analysis - A toolbox for the use with Matlab",
1996-2014.
Further references
on AR modeling and model order selection.
More
about: the performance
of used algorithms; downloading, changelog.
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