时序预测 | MATLAB实现基于CNN-GRU卷积门控循环单元的时间序列预测-递归预测未来(多指标评价)
目录
预测结果
基本介绍
MATLAB实现基于CNN-GRU卷积门控循环单元的时间序列预测-递归预测未来(多指标评价)
1.MATLAB实现基于CNN-GRU卷积门控循环单元的时间序列预测-递归预测未来(多指标评价)；
2.运行环境Matlab2020及以上，data为数据集，单变量时间序列预测；
3.递归预测未来数据，可以控制预测未来大小的数目，适合循环性、周期性数据预测；
4.命令窗口输出R2、MAE、MAPE、MBE、MSE等评价指标；
程序设计
- 完整程序和数据获取方式：私信博主回复MATLAB实现基于CNN-GRU卷积门控循环单元的时间序列预测-递归预测未来(多指标评价)；
matlab
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 数据集分析
outdim = 1; % 最后一列为输出
num_size = 0.7; % 训练集占数据集比例
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 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);
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原文链接：https://blog.csdn.net/kjm13182345320/article/details/132093256参考资料
[1] https://blog.csdn.net/kjm13182345320/article/details/129036772?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128690229