时序预测 | Matlab基于灰色隐马尔可夫模型(HMMP-GM11)的时间序列预测

预测效果

基本介绍

灰色HMMP-GM11改进模型,通过引入隐马尔可夫模型(HMM)来对原始数据进行状态分析，然后利用GM(1,1)模型进行预测，从而提高了预测精度。并采用变量筛选MIV方法对变量进行筛选，对每个指标的重要性进行分析。内附具体流程步骤

程序设计

• 完整源码和数据下载地址私信回复Matlab基于灰色隐马尔可夫模型(HMMP-GM11)的时间序列预测。

%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------%%  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%%  数据反归一化
T_sim1 = mapminmax('reverse', t_sim1', ps_output);
T_sim2 = mapminmax('reverse', t_sim2', ps_output);%% V. 评价指标
%%  均方根误差 RMSE
error1 = sqrt(sum((T_sim1 - T_train).^2)./M);
error2 = sqrt(sum((T_test - T_sim2).^2)./N);%% 决定系数
R1 = rsquare(T_train,T_sim1);
R2 = rsquare(T_test,T_sim2);MAE1 = mean(abs(T_train - T_sim1));
MAE2 = mean(abs(T_test - T_sim2));
%% 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));
%-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/124693040?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/124864369?spm=1001.2014.3001.5502