ar1.m

交互小波分析及其一致性分析

function [g,a,mu2]=ar1(x)
% AR1 - Allen and Smith AR(1) model estimation.
% Syntax: [g,a,mu2]=ar1(x);
%
% Input:  x - time series (univariate).
%
% Output: g - estimate of the lag-one autocorrelation.
%         a - estimate of the noise variance.
%         mu2 - estimated square on the mean.
%
% AR1 uses the algorithm described by Allen and Smith 1995, except
% that Matlab's 'fzero' is used rather than Newton-Raphson.
%
% Fzero in general can be rather picky - although
% I haven't had any problem with its implementation
% here, I recommend occasionally checking the output
% against the simple estimators in AR1NV.
%
% Alternative AR(1) estimatators: ar1cov, ar1nv, arburg, aryule
%
% Written by Eric Breitenberger.      Version 1/21/96
% Please send comments and suggestions to [e-mail]
%
% Updated,optimized&stabilized by Aslak Grinsted 2003-2005
%


x=x(:);
N=length(x);
m=mean(x);
x=x-m;

% Lag zero and one covariance estimates:
c0=x'*x/N;
c1=x(1:N-1)'*x(2:N)/(N-1);


g0=c1/c0; % Initial estimate for gamma

o=optimset('fzero');
o.TolX=0.0001;
o.Display='off';

% Find g by getting zero of 'gammest':
g=fzero(@gammest,g0,o); %-updated to accomodate newer versions of matlab
if isnan(g) %optimization failed use homemade zero finder... (aslak 2005)
    ginout=[g0*.999;g0];
    ginout(:,2)=[gammest(ginout(1,1)); gammest(ginout(2,1));];
	ii=0;
    while (ii<10)&(abs(ginout(end,2))>1e-6)
		ii=ii+1;
        dx=ginout(end,1)-ginout(end-1,1);
        dy=ginout(end,2)-ginout(end-1,2);
        xguess=ginout(end,1)-ginout(end,2)*dx/dy; %linear extrap
        ginout(end+1,:)=[xguess gammest(xguess)];
    end
    [m,mi]=nanmin(abs(ginout(:,2)));
    g=ginout(mi,1);
    if (m>1e-6)
        g=nan;
    end
end

gk=1:N-1;
gk=g.^gk;
mu2=(1/N)+(1/N^2)*2*sum((N-1:-1:1).*gk);

c0est=c0/(1-mu2);
a=sqrt((1-g^2)*c0est);


    function gout=gammest(gin)
        % GAMMEST - used by AR1 to compute
        % a function for minimization by fzero.
        %
        % Written by Eric Breitenberger.      Version 1/21/96
        % Please send comments and suggestions to [e-mail]
        %
        
        gk=1:N-1;
        gk=gin.^gk;
        mu2=(1/N)+(2/N^2)*sum((N-1:-1:1).*gk);
        gout=(1-g0)*mu2+g0-gin;
        if gout>1, gout=nan; end
    end
end