基于GA遗传优化的双向LSTM融合多头注意力(BiLSTM-MATT)时间序列预测算法matlab仿真

1.前言

时间序列预测是机器学习领域的重要任务，广泛应用于气象预报 、金融走势分析、工业设备故障预警等场景。传统时间序列模型（如 ARIMA、单 LSTM）在处理长序列依赖、捕捉多尺度特征时存在局限性，而双向LSTM（BiLSTM） 可同时利用历史与未来上下文信息，多头注意力（Multi-Head Attention） 能聚焦关键时间步特征，二者融合的BiLSTM-MATT算法有效解决了上述问题，成为当前高精度时间序列预测的主流方案之一。

2.算法运行效果图预览

(完整程序运行后无水印)

3.算法运行软件版本

Matlab2024b(推荐)或者matlab2022a

4.部分核心程序

（完整版代码包含中文注释和操作步骤视频）

[V,I] = min(JJ);
X     = phen1(I,:);
 
LR             = X(1);
numHiddenUnits = floor(X(2))+1;% 定义隐藏层中LSTM单元的数量
 
%CNN-GRU-ATT
layers = func_model2(Dim,Dimo,numHiddenUnits);
 
%设置
%迭代次数
%学习率为0.001
options = trainingOptions('adam', ...       
    'MaxEpochs', 3000, ...                 
    'InitialLearnRate', LR, ...          
    'LearnRateSchedule', 'piecewise', ...  
    'LearnRateDropFactor', 0.1, ...        
    'LearnRateDropPeriod', 1000, ...        
    'Shuffle', 'every-epoch', ...          
    'Plots', 'training-progress', ...     
    'Verbose', false);
 
%训练
[Net,INFO] = trainNetwork(Nsp_train2, NTsp_train, layers, options);
 
%数据预测
Dpre1 = predict(Net, Nsp_train2);
Dpre2 = predict(Net, Nsp_test2);
 
%归一化还原
T_sim1=Dpre1*Vmax2;
T_sim2=Dpre2*Vmax2;
 
Tat_train=Tat_train-mean(Tat_train);
T_sim1=T_sim1-mean(T_sim1);
Tmax1 = max(Tat_train);
Tmax2 = max(T_sim1);
T_sim1=Tmax1*T_sim1/Tmax2;
 
 
Tat_test=Tat_test-mean(Tat_test);
T_sim2=T_sim2-mean(T_sim2);
T2max1 = max(Tat_test);
T2max2 = max(T_sim2);
T_sim2=T2max1*T_sim2/T2max2;
%网络结构
analyzeNetwork(Net)
 
 
figure
subplot(211);
plot(1: Num1, Tat_train,'-bs',...
    'LineWidth',1,...
    'MarkerSize',6,...
    'MarkerEdgeColor','k',...
    'MarkerFaceColor',[0.9,0.0,0.0]);
hold on
plot(1: Num1, T_sim1,'g',...
    'LineWidth',2,...
    'MarkerSize',6,...
    'MarkerEdgeColor','k',...
    'MarkerFaceColor',[0.9,0.9,0.0]);
 
legend('真实值', '预测值')
xlabel('预测样本')
ylabel('预测结果')
grid on
 
subplot(212);
plot(1: Num1, Tat_train-T_sim1','-bs',...
    'LineWidth',1,...
    'MarkerSize',6,...
    'MarkerEdgeColor','k',...
    'MarkerFaceColor',[0.9,0.0,0.0]);
 
xlabel('预测样本')
ylabel('预测误差')
grid on
ylim([-50,50]);
figure
subplot(211);
plot(1: Num2, Tat_test,'-bs',...
    'LineWidth',1,...
    'MarkerSize',6,...
    'MarkerEdgeColor','k',...
    'MarkerFaceColor',[0.9,0.0,0.0]);
hold on
plot(1: Num2, T_sim2,'g',...
    'LineWidth',2,...
    'MarkerSize',6,...
    'MarkerEdgeColor','k',...
    'MarkerFaceColor',[0.9,0.9,0.0]);
legend('真实值', '预测值')
xlabel('测试样本')
ylabel('测试结果')
grid on
subplot(212);
plot(1: Num2, Tat_test-T_sim2','-bs',...
    'LineWidth',1,...
    'MarkerSize',6,...
    'MarkerEdgeColor','k',...
    'MarkerFaceColor',[0.9,0.0,0.0]);
 
xlabel('预测样本')
ylabel('预测误差')
grid on
ylim([-50,50]);
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5.算法理论概述

传统单向LSTM仅能从历史到未来（左→右）处理序列，无法利用“未来”上下文（如预测第t步时，无法参考t+1、t+2步的信息），而BiLSTM通过并行部署“正向LSTM”与“反向LSTM”，将双向信息融合为统一的时序特征表示.