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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd"><html><head> <title>Description of contents</title> <meta name="keywords" content="contents"> <meta name="description" content="Time Series Analysis - A toolbox for the use with Matlab and Octave."> <meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1"> <meta name="generator" content="m2html © 2003 Guillaume Flandin"> <meta name="robots" content="index, follow"> <link type="text/css" rel="stylesheet" href="../m2html.css"></head><body><a name="_top"></a><div><a href="../index.html">Home</a> > <a href="index.html">tsa</a> > contents.m</div><!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png"> Master index</a></td><td align="right"><a href="index.html">Index for tsa <img alt=">" border="0" src="../right.png"></a></td></tr></table>--><h1>contents</h1><h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>Time Series Analysis - A toolbox for the use with Matlab and Octave.</strong></div><h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>This is a script file. </strong></div><h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="fragment"><pre class="comment"> Time Series Analysis - A toolbox for the use with Matlab and Octave.
Copyright (C) 1996-2004 by Alois Schloegl <a.schloegl@ieee.org>
WWW: http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/tsa/ $Revision: 1.14 $ $Id: contents.m,v 1.14 2004/07/05 09:13:45 schloegl Exp $
LICENSE:
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program 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 General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
Time Series Analysis - a toolbox for the use with Matlab aar adaptive autoregressive estimator acovf (*) Autocovariance function acorf (acf) (*) autocorrelation function pacf (*) partial autocorrelation function, includes signifcance test and confidence interval parcor (*) partial autocorrelation function biacovf biautocovariance function (3rd order cumulant) bispec Bi-spectrum durlev (*) solves Yule-Walker equation - converts ACOVF into AR parameters lattice (*) calcultes AR parameters with lattice method lpc (*) calculates the prediction coefficients form a given time series invest0 (*) a prior investigation (used by invest1) invest1 (*) investigates signal (useful for 1st evaluation of the data) selmo (*) Select Order of Autoregressive model using different criteria histo (*) histogram hup (*) test Hurwitz polynomials ucp (*) test Unit Circle Polynomials y2res (*) computes mean, variance, skewness, kurtosis, entropy, etc. from data series ar_spa (*) spectral analysis based on the autoregressive model detrend (*) removes trend, can handle missing values, non-equidistant sampled data flix floating index, interpolates data for non-interger indices Multivariate analysis adim adaptive information matrix (inverse correlation matrix) mvar multivariate (vector) autoregressive estimation mvaar multivariate adaptvie autoregressive estimation using Kalman filtering mvfilter multivariate filter mvfreqz multivariate spectra arfit2 provides compatibility to ARFIT [Schneider and Neumaier, 2001] Conversions between Autocorrelation (AC), Autoregressive parameters (AR), prediction polynom (POLY) and Reflection coefficient (RC) ac2poly (*) transforms autocorrelation into prediction polynom ac2rc (*) transforms autocorrelation into reflexion coefficients ar2rc (*) transforms autoregressive parameters into reflection coefficients rc2ar (*) transforms reflection coefficients into autoregressive parameters poly2ac (*) transforms polynom to autocorrelation poly2ar (*) transforms polynom to AR poly2rc (*) rc2ac (*) rc2poly (*) ar2poly (*) Utility functions sinvest1 shows the parameter calculated by INVEST1 Test suites tsademo demonstrates INVEST1 on EEG data invfdemo demonstration of matched, inverse filtering bisdemo demonstrates bispectral estimation (*) indicates univariate analysis of multiple data series (each in a row) can be processed. (-) indicates that these functions will be removed in future REFERENCES (sources): http://www.itl.nist.gov/ http://mathworld.wolfram.com/ P.J. Brockwell and R.A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991. O. Foellinger "Lineare Abtastsysteme", Oldenburg Verlag, Muenchen, 1986. F. Gausch "Systemtechnik", Textbook, University of Technology Graz, 1993. M.S. Grewal and A.P. Andrews "Kalman Filtering" Prentice Hall, 1993. S. Haykin "Adaptive Filter Theory" 3ed. Prentice Hall, 1996. E.I. Jury "Theory and Application of the z-Transform Method", Robert E. Krieger Publishing Co., 1973. M.S. Kay "Modern Spectal Estimation" Prentice Hall, 1988. Ch. Langraf and G. Schneider "Elemente der Regeltechnik", Springer Verlag, 1970. S.L. Marple "Digital Spetral Analysis with Applications" Prentice Hall, 1987. C.L. Nikias and A.P. Petropulu "Higher-Order Spectra Analysis" Prentice Hall, 1993. M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981. T. Schneider and A. Neumaier "Algorithm 808: ARFIT - a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models" ACM Transactions on Mathematical software, 27(Mar), 58-65. C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963). W.S. Wei "Time Series Analysis" Addison Wesley, 1990. REFERENCES (applications): [1] A. Schl鰃l, B. Kemp, T. Penzel, D. Kunz, S.-L. Himanen,A. V鋜ri, G. Dorffner, G. Pfurtscheller. Quality Control of polysomnographic Sleep Data by Histogram and Entropy Analysis. Clin. Neurophysiol. 1999, Dec; 110(12): 2165 - 2170. [2] Penzel T, Kemp B, Kl鰏ch G, Schl鰃l A, Hasan J, Varri A, Korhonen I. Acquisition of biomedical signals databases IEEE Engineering in Medicine and Biology Magazine 2001, 20(3): 25-32 [3] Alois Schl鰃l (2000) The electroencephalogram and the adaptive autoregressive model: theory and applications Shaker Verlag, Aachen, Germany,(ISBN3-8265-7640-3). Features: - Multiple Signal Processing - Efficient algorithms - Model order selection tools - higher (3rd) order analysis - Maximum entropy spectral estimation - can deal with missing values (NaN's)</pre></div><!-- crossreference --><h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>This function calls:<ul style="list-style-image:url(../matlabicon.gif)"></ul>This function is called by:<ul style="list-style-image:url(../matlabicon.gif)"></ul><!-- crossreference --><hr><address>Generated on Tue 17-Aug-2004 00:13:21 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> © 2003</address></body></html>⌨️ 快捷键说明
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