基于MATLAB实现的ARIMA模型做时间序列分析预测+使用说明文档.zip

% function [z, warningInfo] = ARIMA(step, thr, x) clc,clear step=10; thr=13; x=[13.0462 13.1675 12.0852 12.2757 12.9838 13.0659 12.095 12.2155 13.075 13.0592 12.1285 12.216 13.0099 13.0856 12.0967 12.2402 13.1164 13.1458 12.1204 12.2715 12.9135 13.1579 11.9901 12.3195 13.0491 13.1174 12.132 12.2866 12.9728 13.1781 12.033 12.3587 12.955 13.1267 12.0681 12.3077 12.9272 13.0996 12.0186 12.2959 12.9143 13.0309 12.0159 12.3032 12.979 13.0986 12.0494 12.3256 13.054 13.1369 12.1349 12.3698 13.1919 13.1832 12.2351 12.3992 13.1689 13.1098 12.1985 12.3432 13.1132 13.1738 12.2001 12.3857 13.0556 13.138 12.1679 12.3797 13.0038 13.2089 12.1035 12.4219 13.009 13.1989 12.1198 12.4136 12.9219 13.1948 12.0373 12.4077 12.952 13.1925 12.0594 12.399 13.0691 13.221 12.1832 12.4307 13.0837 13.2001 12.2188 12.4282 12.9957 13.1869 12.1024 12.4132 12.9479 13.1756 12.0827 12.3989 12.9798 13.1602 12.2986 12.3921 13.2626 13.1756 12.2271 12.3719 12.9891 13.1339]; r11=autocorr(x); %计算自相关函数 r12=parcorr(x); %计算偏相关函数 dx=diff(x); %计算1 阶差分 r21=autocorr(dx); %计算自相关函数 r22=parcorr(dx); %计算偏相关函数 n=length(dx); %计算差分后的数据个数 if n<20 warningInfo=1002; %disp(warningInfo); return; else k=1; for i=0:3 for j=0:3 spec= garchset('R',i,'M',j,'Display','off'); %指定模型的结构 [coeffX,errorsX,LLFX] = garchfit(spec,dx); %拟合参数 num=garchcount(coeffX); %计算拟合参数的个数 [aic,bic]=aicbic(LLFX,num,n); ASM(k,:)=[aic,i,j]; k=k+1;%fprintf('R=%d,M=%d,AIC=%f,BIC=%f\n',i,j,aic,bic); end end s=min(ASM(:,1)); [m1,n1]=find(ASM(:,1)==s); r=ASM(m1,2);m=ASM(m1,3); spec2= garchset('R',r,'M',m,'Display','off'); %指定模型的结构 [coeffX,errorsX,LLFX] = garchfit(spec2,dx); %拟合参数 [sigma,w] = garchpred(coeffX,dx,step); %计算10 步预报值 %sigma z=x(end)+cumsum(w);%计算原始数据的10 步预测值 % x2=a(end)+w % spec3=garchset('R',r,'M',m,'Display','off'); % [coeffX,errorsX,LLFX] = garchfit(spec3,a); % [sigma2,w2] = garchpred(coeffX,a,10); % y=sigma2+w2 warningInfo=1000; for i=1:step if z(i)>thr %当预测的某个值超过阈值 warningInfo=i; break else end end %disp(warningInfo); % end