Transformer-LSTM praedictio |

2024-07-12

Transformer-LSTM praedictio | . Matlab instrumentorum Transformer-LSTMMultivariate temporis series praevidens

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- Transformer-LSTM praedictio |

Effectus album

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basic introductio

1.Matlab instrumentum Transformer-LSTM series multi-variabilis temporis vaticinium, Transformator LSTM componit breve tempus memoriam longam.Neural NetworksSeriem temporis multivariatam praevidens;

2. Operans ambitus Matlab2023b et supra est;

3.data isnotitia paro, input multiplices notas, unam variabilem output, considera influentiam notarum historicarum, multi-variabilis temporis series vaticinium, main.m est propositum principale, modo currendum est, omnia fasciculi in uno folder ponuntur;

4. Praeceptum fenestrae outputs multiplex index aestimationes ut R2, MSE, RMSE, MAE, MAPE, MBE;

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programming

Complete programma ac notitia download privata nuntius blogger responsaMatlab instrumentorum seriei temporis multi-variabilis Transformer-LSTM praedictio。



%%  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
result = xlsread('data.xlsx');

%%  数据分析
num_samples = length(result);  % 样本个数
or_dim = size(result, 2);      % 原始特征+输出数目
kim =  2;                      % 延时步长（kim个历史数据作为自变量）
zim =  1;                      % 跨zim个时间点进行预测



%%  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
num_train_s = round(num_size * num_samples); % 训练集样本个数
f_ = size(res, 2) - outdim;                  % 输入特征维度


%%  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);

%%  数据平铺
P_train =  double(reshape(P_train, f_, 1, 1, M));
P_test  =  double(reshape(P_test , f_, 1, 1, N));

t_train = t_train';
t_test  = t_test' ;

%%  数据格式转换
for i = 1 : M
    p_train{i, 1} = P_train(:, :, 1, i);
end

for i = 1 : N
    p_test{i, 1}  = P_test( :, :, 1, i);
end


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References

[1] https://blog.csdn.net/kjm13182345320/article/details/128163536?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128151206?spm=1001.2014.3001.5502

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