greymethod.m

是Grey Method的使用方法

% 灰色预测 GM(1,1)代码
                  [y,p]=gm_1_1([200 250 300 350],2)
                  function [y,p,e]=gm_1_1(X,k) 
                  %Build the calculating dieplate for the typical gray model. 

                  %Example [y,p]=gm_1_1([200 250 300 350],2) 

                  %Designed by NIXIUHUI,Dalian Fisher University. 
                  %20 April,2004. Last modified by NXH at 25 September,2004   
 

                  if nargout>3,error('Too many output argument.');end 
                  if nargin==1,k=1;x_orig=X; 
                  elseif nargin==0|nargin>2 
                     error('Wrong number of input arguments.'); 
                  end 
                  x_orig=X; 
                  predict=k; 

                  %AGO process 
                  x=cumsum(x_orig); 


                  %compute the coefficient(a and u)------------------------ 

                  n=length(x_orig); 
                  %first generate the matrix B 
                  for i=1:n-1 
                  B(i)=-(x(i)+x(i+1))/2; 
                  end ;
                  B=[B' ones(n-1,1)]; 
                  %then generate the matrix Y 
                  for i=1n-1); 
                  y(i)=x_orig(i+1); 
                  end 
                  Y=y'; 
                  %get the coefficient. a=au(1) u=au(2) 
                  au=(inv(B'*B))*(B'*Y); 
                  %-------------------------------------------------------- 

                  %change the grey model to symbolic expression 
                  coef1=au(2)/au(1); 
                  coef2=x_orig(1)-coef1; 
                  coef3=0-au(1); 
                  costr1=num2str(coef1); 
                  costr2=num2str(abs(coef2)); 
                  costr3=num2str(coef3); 
                  eq=strcat(costr1,'+',costr2,'e^',costr3,'*(t-1))'); 



                  %comparison of calculated and observed value 
                  for t=1:n+predict 
                     mcv(t)=coef1+coef2*exp(coef3*(t-1)); 
                  end 
                  x_mcv0=diff(mcv); 
                  x_mcve=[x_orig(1) x_mcv0]; 
                  x_mcv=diff(mcv(1:end-predict)); 
                  x_orig_n=x_orig(2:end); 
                  x_c_error=x_orig_n-x_mcv; 
                  x_error=mean(abs(x_c_error./x_orig_n)); 

                  if x_error>0.2 
                     disp('model disqualification!'); 
                  elseif x_error>0.1 
                     disp('model check out'); 
                  else 
                     disp('model is perfect!'); 
                  end 
                  %predicting model and plot gragh 
                  plot(1:n,x_orig,'diamond',1:n+predict,x_mcve); 
                  p=x_mcve(end-predict+1:end); 
                  xlabel('CURVE OF GREY MODEL ANALYSIS'); 
                  title('GM(1,1)'); 
                  grid on 
                  y=eq; 
                  e=x_error; 
                  p=x_mcve(end-predict+1:end);
[y,p]=gm_1_1([200 250 300 350],2)