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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 content</title> <meta name="keywords" content="content"> <meta name="description" content="Time Series Analysis (Ver 3.20)"> <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> > content.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>content</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 (Ver 3.20)</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 (Ver 3.20) Schloegl A. (1996-2003) Time Series Analysis - A Toolbox for the use with Matlab. WWW: http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/tsa/ Version 3.20 Date: 10 May 2003 Copyright (C) 1996-2003 by Alois Schloegl <a.schloegl@ieee.org>
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 (planned in future) mvar multivariate (vector) autoregressive estimation mvfilter multivariate filter arfit2 provides compatibility to ARFIT [Schneider and Neumaier, 2001]</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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