【股价预测】基于matlab极限学习机结合集成学习ELM-Adaboost股价预测【含Matlab源码 4634期】.zip

%% 清空环境变量 warning off % 关闭报警信息 close all % 关闭开启的图窗 clear % 清空变量 clc % 清空命令行 %% 数据读取 data=xlsread('数据.xlsx','Sheet1','B2:B900'); %%使用xlsread函数读取EXCEL中对应范围的数据即可 n=10; [input,output]=data_process(data',n);%前n个时刻 预测下一个时刻 N=length(output); %全部样本数目 testNum=50; %设定测试样本数目 trainNum=N-testNum; %计算训练样本数目 %% 划分训练集、测试集 P_train = input(1:trainNum,:)'; T_train =output(1:trainNum)'; P_test =input(trainNum+1:trainNum+testNum,:)'; T_test =output(trainNum+1:trainNum+testNum)'; M = size(P_train, 2); N = size(P_test, 2); %% 数据归一化 [p_train, ps_input] = mapminmax(P_train, 0, 1); p_test = mapminmax('apply', P_test, ps_input); [t_train, ps_output] = mapminmax(T_train, 0, 1); t_test = mapminmax('apply', T_test, ps_output); %% 创建模型 num_hiddens = 50; % 隐藏层节点个数 activate_model = 'sig'; % 激活函数 [IW, B, LW, TF, TYPE] = elmtrain(p_train, t_train, num_hiddens, activate_model, 0); %% 仿真测试 %% 创建/训练 K = 30; % 弱回归器个数 %% 权重初始化 D = ones(1, M) / M; %% 弱回归器回归 for i = 1 : K %% 训练模型 t_sim1(i,:) = elmpredict(p_train, IW, B, LW, TF, TYPE); t_sim2(i,:) = elmpredict(p_test , IW, B, LW, TF, TYPE); %% 预测误差 Error(i, :) = t_sim2(i, :) - T_test; %% 调整D值 weight(i) = 0; for j = 1 : N if abs(Error(i, j)) > 0.02 weight(i) = weight(i) + D(i, j); D(i + 1, j) = D(i, j) * 1.1; else D(i + 1, j) = D(i, j); end end %% 弱回归器i权重 weight(i) = 0.5 / exp(abs(weight(i))); %% D值归一化 D(i + 1, :) = D(i + 1, :) / sum(D(i + 1, :)); end %% 强预测器预测 weight = weight / sum(weight); %% 强回归器输出结果 T_sim1 = zeros(1, M); T_sim2 = zeros(1, N); for i = 1 : K output1 = (weight(i) * t_sim1(i, :)); output2 = (weight(i) * t_sim2(i, :)); T_sim1 = output1 + T_sim1; T_sim2 = output2 + T_sim2; end %% 数据反归一化 T_sim1 = mapminmax('reverse', T_sim1, ps_output); T_sim2 = mapminmax('reverse', T_sim2, ps_output); %% 均方根误差 error1 = sqrt(sum((T_sim1 - T_train).^2) ./ M); error2 = sqrt(sum((T_sim2 - T_test ).^2) ./ N); %% 绘图 figure plot(1: M, T_train, 'r-*', 1: M, T_sim1, 'b-o', 'LineWidth', 1) legend('真实值', '预测值') xlabel('预测样本') ylabel('预测结果') string = {'训练集预测结果对比'; ['RMSE=' num2str(error1)]}; title(string) xlim([1, M]) grid figure plot(1: N, T_test, 'r-*', 1: N, T_sim2, 'b-o', 'LineWidth', 1) legend('真实值', '预测值') xlabel('预测样本') ylabel('预测结果') string = {'测试集预测结果对比'; ['RMSE=' num2str(error2)]}; title(string) xlim([1, N]) grid %% 相关指标计算 % R2 R1 = 1 - norm(T_train - T_sim1)^2 / norm(T_train - mean(T_train))^2; R2 = 1 - norm(T_test - T_sim2)^2 / norm(T_test - mean(T_test ))^2; disp(['训练集数据的R2为：', num2str(R1)]) disp(['测试集数据的R2为：', num2str(R2)]) % MAE mae1 = sum(abs(T_sim1 - T_train)) ./ M ; mae2 = sum(abs(T_sim2 - T_test )) ./ N ; disp(['训练集数据的MAE为：', num2str(mae1)]) disp(['测试集数据的MAE为：', num2str(mae2)]) % MBE mbe1 = sum(T_sim1 - T_train) ./ M ; mbe2 = sum(T_sim2 - T_test ) ./ N ; disp(['训练集数据的MBE为：', num2str(mbe1)]) disp(['测试集数据的MBE为：', num2str(mbe2)]) %% 绘制散点图 sz = 25; c = 'b'; figure scatter(T_train, T_sim1, sz, c) hold on plot(xlim, ylim, '--k') xlabel('训练集真实值'); ylabel('训练集预测值'); xlim([min(T_train) max(T_train)]) ylim([min(T_sim1) max(T_sim1)]) title('训练集预测值 vs. 训练集真实值') figure scatter(T_test, T_sim2, sz, c) hold on plot(xlim, ylim, '--k') xlabel('测试集真实值'); ylabel('测试集预测值'); xlim([min(T_test) max(T_test)]) ylim([min(T_sim2) max(T_sim2)]) title('测试集预测值 vs. 测试集真实值')