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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 invest0</title> <meta name="keywords" content="invest0"> <meta name="description" content="First Investigation of a signal (time series) - automated part"> <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> > invest0.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>invest0</h1><h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>First Investigation of a signal (time series) - automated part</strong></div><h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>function [AutoCov,AutoCorr,MX,E,NC]=invest0(Y,Pmax,Mode); </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"> First Investigation of a signal (time series) - automated part [AutoCov,AutoCorr,ARPMX,E,ACFsd,NC]=invest0(Y,Pmax); [AutoCov,AutoCorr,ARPMX,E,ACFsd,NC]=invest0(AutoCov,Pmax,Mode); Y time series Pmax maximal order (optional) AutoCov Autocorrelation AutoCorr normalized Autocorrelation PartACF Partial Autocorrelation ARPMX Autoregressive Parameter for order Pmax-1 E Error function E(p) NC Number of values (length-missing values) REFERENCES: P.J. Brockwell and R.A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991. M.S. Grewal and A.P. Andrews "Kalman Filtering" Prentice Hall, 1993. S. Haykin "Adaptive Filter Theory" 3ed. Prentice Hall, 1996. M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981. W.S. Wei "Time Series Analysis" Addison Wesley, 1990.</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)"><li><a href="acovf.html" class="code" title="function [ACF,NN] = acovf(Z,KMAX,Mode,Mode2);">acovf</a> ACOVF estimates autocovariance function (not normalized)</li><li><a href="lattice.html" class="code" title="function [MX,PE,arg3] = lattice(Y,lc,Mode);">lattice</a> Estimates AR(p) model parameter with lattice algorithm (Burg 1968)</li><li><a href="sumskipnan.html" class="code" title="function [o,count,SSQ,S4M] = sumskipnan(i,DIM)">sumskipnan</a> SUMSKIPNAN adds all non-NaN values.</li></ul>This function is called by:<ul style="list-style-image:url(../matlabicon.gif)"><li><a href="invest1.html" class="code" title="function [AutoCov,AutoCorr,ARPMX,E,C,s]=invest1(Y,Pmax,D);">invest1</a> First Investigation of a signal (time series) - interactive</li></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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