SVM神经网络的信息粒化时序回归预测

%%SVM神经网络的信息粒化时序回归预测----上证指数开盘指数变化趋势和变化空间预测%%清空环境变量functionchapter15tic;closeall;clear;clc;formatcompact;%%原始数据的提取%载入测试数据上证指数(1990.12.19-2009.08.19)%数据是一个4579*6的double型的矩阵,每一行表示每一天的上证指数%6列分别表示当天上证指数的开盘指数,指数最高值,指数最低值,收盘指数,当日交易量,当日交易额.loadchapter15_sh.mat;%提取数据ts=sh_open;time=length(ts);%画出原始上证指数的每日开盘数figure;plot(ts,'LineWidth',2);title('上证指数的每日开盘数(1990.12.20-2009.08.19)','FontSize',12);xlabel('交易日天数(1990.12.19-2009.08.19)','FontSize',12);ylabel('开盘数','FontSize',12);gridon;print-dtiff-r600original;snapnow;%%对原始数据进行模糊信息粒化win_num=floor(time/5);tsx=1:win_num;tsx=tsx';[Low,R,Up]=FIG_D(ts','triangle',win_num);%模糊信息粒化可视化图figure;holdon;plot(Low,'b+');plot(R,'r*');plot(Up,'gx');holdoff;legend('Low','R','Up',2);title('模糊信息粒化可视化图','FontSize',12);xlabel('粒化窗口数目','FontSize',12);ylabel('粒化值','FontSize',12);gridon;print-dtiff-r600FIGpic;snapnow;%%利用SVM对Low进行回归预测%数据预处理,将Low进行归一化处理%mapminmax为matlab自带的映射函数[low,low_ps]=mapminmax(Low);low_ps.ymin=100;low_ps.ymax=500;%对Low进行归一化[low,low_ps]=mapminmax(Low,low_ps);%画出Low归一化后的图像figure;plot(low,'b+');title('Low归一化后的图像','FontSize',12);xlabel('粒化窗口数目','FontSize',12);ylabel('归一化后的粒化值','FontSize',12);gridon;print-dtiff-r600lowscale;%对low进行转置,以符合libsvm工具箱的数据格式要求low=low';snapnow;%选择回归预测分析中最佳的SVM参数c&g%首先进行粗略选择[bestmse,bestc,bestg]=SVMcgForRegress(low,tsx,-10,10,-10,10,3,1,1,0.1,1);%打印粗略选择结果disp('打印粗略选择结果');str=sprintf('SVMparametersforLow:BestCrossValidationMSE=%gBestc=%gBestg=%g',bestmse,bestc,bestg);disp(str);%根据粗略选择的结果图再进行精细选择[bestmse,bestc,bestg]=SVMcgForRegress(low,tsx,-4,8,-10,10,3,0.5,0.5,0.05,1);%打印精细选择结果disp('打印精细选择结果');str=sprintf('SVMparametersforLow:BestCrossValidationMSE=%gBestc=%gBestg=%g',bestmse,bestc,bestg);disp(str);%训练SVMcmd=['-c',num2str(bestc),'-g',num2str(bestg),'-s3-p0.1'];low_model=svmtrain(low,tsx,cmd);%预测[low_predict,low_mse]=svmpredict(low,tsx,low_model);low_predict=mapminmax('reverse',low_predict,low_ps);predict_low=svmpredict(1,win_num+1,low_model);predict_low=mapminmax('reverse',predict_low,low_ps);predict_low%%对于Low的回归预测结果分析figure;holdon;plot(Low,'b+');plot(low_predict,'r*');legend('originallow','predictlow',2);title('originalvspredict','FontSize',12);xlabel('粒化窗口数目','FontSize',12);ylabel('粒化值','FontSize',12);gridon;print-dtiff-r600lowresult;figure;error=low_predict-Low';plot(error,'ro');title('误差(predicteddata-originaldata)','FontSize',12);xlabel('粒化窗口数目','FontSize',12);ylabel('误差量','FontSize',12);gridon;print-dtiff-r600lowresulterror;snapnow;%%利用SVM对R进行回归预测%数据预处理,将R进行归一化处理%mapminmax为matlab自带的映射函数[r,r_ps]=mapminmax(R);r_ps.ymin=100;r_ps.ymax=500;%对R进行归一化[r,r_ps]=mapminmax(R,r_ps);%画出R归一化后的图像figure;plot(r,'r*');title('r归一化后的图像','FontSize',12);gridon;%对R进行转置,以符合libsvm工具箱的数据格式要求r=r';snapnow;%选择回归预测分析中最佳的SVM参数c&g%首先进行粗略选择[bestmse,bestc,bestg]=SVMcgForRegress(r,tsx,-10,10,-10,10,3,1,1,0.1);%打印粗略选择结果disp('打印粗略选择结果');str=sprintf('SVMparametersforR:BestCrossValidationMSE=%gBestc=%gBestg=%g',bestmse,bestc,bestg);disp(str);%根据粗略选择的结果图再进行精细选择[bestmse,bestc,bestg]=SVMcgForRegress(r,tsx,-4,8,-10,10,3,0.5,0.5,0.05);%打印精细选择结果disp('打印精细选择结果');str=sprintf('SVMparametersforR:BestCrossValidationMSE=%gBestc=%gBestg=%g',bestmse,bestc,bestg);disp(str);%训练SVMcmd=['-c',num2str(bestc),'-g',num2str(bestg),'-s3-p0.1'];r_model=svmtrain(r,tsx,cmd);%预测[r_predict,r_mse]=svmpredict(r,tsx,low_model);r_predict=mapminmax('reverse',r_predict,r_ps);predict_r=svmpredict(1,win_num+1,r_model);predict_r=mapminmax('reverse',predict_r,r_ps);predict_r%%对于R的回归预测结果分析figure;holdon;plot(R,'b+');plot(r_predict,'r*');legend('originalr','predictr',2);title('originalvspredict','FontSize',12);gridon;figure;error=r_predict-R';plot(error,'ro');title('误差(predicteddata-originaldata)','FontSize',12);gridon;snapnow;%%利用SVM对Up进行回归预测%数据预处理,将up进行归一化处理%mapminmax为matlab自带的映射函数[up,up_ps]=mapminmax(Up);up_ps.ymin=100;up_ps.ymax=500;%对Up进行归一化[up,up_ps]=mapminmax(Up,up_ps);%画出Up归一化后的图像figure;plot(up,'gx');title('Up归一化后的图像','FontSize',12);gridon;%对up进行转置,以符合libsvm工具箱的数据格式要求up=up';snapnow;%选择回归预测分析中最佳的SVM参数c&g%首先进行粗略选择[bestmse,bestc,bestg]=SVMcgForRegress(up,tsx,-10,10,-10,10,3,1,1,0.5);%打印粗略选择结果disp('打印粗略选择结果');str=sprintf('SVMparametersforUp:BestCrossValidationMSE=%gBestc=%gBestg=%g',bestmse,bestc,bestg);disp(str);%根据粗略选择的结果图再进行精细选择[bestmse,bestc,bestg]=SVMcgForRegress(up,tsx,-4,8,-10,10,3,0.5,0.5,0.2);%打印精细选择结果disp('打印精细选择结果');str=sprintf('SVMparametersforUp:BestCrossValidationMSE=%gBestc=%gBestg=%g',bestmse,bestc,bestg);disp(str);%训练SVMcmd=['-c',num2str(bestc),'-g',num2str(bestg),'-s3-p0.1'];up_model=svmtrain(up,tsx,cmd);%预测[up_predict,up_mse]=svmpredict(up,tsx,up_model);up_predict=mapminmax('reverse',up_predict,up_ps);predict_up=svmpredict(1,win_num+1,up_model);predict_up=mapminmax('reverse',predict_up,up_ps);predict_up%%对于Up的回归预测结果分析figure;holdon;plot(Up,'b+');plot(up_predict,'r*');legend('originalup','predictup',2);title('originalvspredict','FontSize',12);gridon;figure;error=up_predict-Up';plot(error,'ro');title('误差(predicteddata-originaldata)','FontSize',12);gridon;toc;snapnow;%%子函数SVMcgForRegress.mfunction[mse,bestc,be