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 onsubplot(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 onsubplot(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]);
229

5.算法仿真参数

MAXGEN = 15;
NIND   = 10;
Nums   = 2; 
Chrom  = crtbp(NIND,Nums*10);%sh
Areas = [];
for i = 1:1Areas = [Areas,[0.00001;0.001]];% 整体学习率
end
for i = 1:1Areas = [Areas,[2;12]];% BILSTM层数
end