acorf.html

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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 acorf</title>  <meta name="keywords" content="acorf">  <meta name="description" content="Calculates autocorrelations for multiple data series.">  <meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">  <meta name="generator" content="m2html &copy; 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> &gt;  <a href="index.html">tsa</a> &gt; acorf.m</div><!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td><td align="right"><a href="index.html">Index for tsa&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>--><h1>acorf</h1><h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>Calculates autocorrelations for multiple data series.</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,stderr,lpq,qpval] = acorf(Z,N); </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">  Calculates autocorrelations for multiple data series.
  Missing values in Z (NaN) are considered. 
  Also calculates Ljung-Box Q stats and p-values.

    [AutoCorr,stderr,lpq,qpval] = acorf(Z,N);
  If mean should be removed use
    [AutoCorr,stderr,lpq,qpval] = acorf(detrend(Z',0)',N);
  If trend should be removed use
    [AutoCorr,stderr,lpq,qpval] = acorf(detrend(Z')',N);

 INPUT
  Z    is data series for which autocorrelations are required
       each in a row
  N    maximum lag

 OUTPUT
  AutoCorr nr x N matrix of autocorrelations
  stderr   nr x N matrix of (approx) std errors
  lpq      nr x M matrix of Ljung-Box Q stats
  qpval    nr x N matrix of p-values on Q stats
   
 All input and output parameters are organized in rows, one row 
 corresponds to one series

 REFERENCES:
  S. Haykin &quot;Adaptive Filter Theory&quot; 3ed. Prentice Hall, 1996.
  M.B. Priestley &quot;Spectral Analysis and Time Series&quot; Academic Press, 1981. 
  W.S. Wei &quot;Time Series Analysis&quot; Addison Wesley, 1990.
  J.S. Bendat and A.G.Persol &quot;Random Data: Analysis and Measurement procedures&quot;, Wiley, 1986.</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></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> &copy; 2003</address></body></html>

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