Stacking集成学习算法的多变量时序预测 Matlab代码

Stacking集成学习算法的多变量时序预测 Matlab代码Matlab2023

目录

• • Stacking集成学习算法的多变量时序预测 Matlab代码Matlab2023
  • • 预测结果
    • 评价指标
    • 基本介绍
    • 程序设计
    • 参考资料

预测结果

评价指标

训练集数据的R2为：0.99805
测试集数据的R2为：0.98351
训练集数据的MAE为：14.8716
测试集数据的MAE为：49.7271
训练集数据的MAPE为：0.0041394
测试集数据的MAPE为：0.014129
训练集数据的MBE为：2.0468
测试集数据的MBE为：12.0079
训练集数据的MSE为：563.9512
测试集数据的MSE为：4964.9945

基本介绍

Stacking集成学习算法的多变量时序预测 Matlab代码Matlab2023

程序设计

%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  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
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

%%  导入数据
re
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  划分训练集和测试集
P_train = res(1: num_train_s, 1: f_)';
T_train = res(1: num_train_s, f_ + 1: end)';
M = size(P_train, 2);

P_test = res(num_train_s + 1: end, 1: f_)';
T_test = res(num_train_s + 1: end, f_ + 1: end)';
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);


%%  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);

%%  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)])
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 平均绝对百分比误差MAPE
MAPE1 = mean(abs((T_train - T_sim1)./T_train));
MAPE2 = mean(abs((T_test - T_sim2)./T_test));

disp(['训练集数据的MAPE为：', num2str(MAPE1)])
disp(['测试集数据的MAPE为：', num2str(MAPE2)])

%  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)])
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%均方误差 MSE
mse1 = sum((T_sim1 - T_train).^2)./M;
mse2 = sum((T_sim2 - T_test).^2)./N;

disp(['训练集数据的MSE为：', num2str(mse1)])
disp(['测试集数据的MSE为：', num2str(mse2)])


%%  绘图
figure
plot(1: M, T_train, '-', 1: M, T_sim1, '-', '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])
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

figure
plot(1: N, T_test, '-', 1: N, T_sim2, '-', '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])

参考资料

[1] https://blog.csdn.net/m0_57362105/category_12278342.html?spm=1001.2014.3001.5482
[2] https://blog.csdn.net/m0_57362105/article/details/129998935