鲸鱼算法优化卷积长短期记忆神经网络WOA-CNN-LSTM股价序列预测【含Matlab源码 3008期】.zip

% CNN-LSTM多变量多步 % 数据集 列为特征，行为样本数目 clc clear load Train.mat % Train(1,:) =[]; y = Train.demand; x = Train{:,3:end}; [xnorm,xopt] = mapminmax(x',0,1); [ynorm,yopt] = mapminmax(y',0,1); x = x'; k = 24; % 滞后长度 % 转换成4-D image for i = 1:length(ynorm)-k Train_xNorm{i} = reshape(xnorm(:,i:i+k-1),6,1,1,k); Train_yNorm(i) = ynorm(i+k-1); end Train_yNorm= Train_yNorm'; %-------------------------------------------------------------------------- load Test.mat Test(1,:) =[]; ytest = Test.demand; xtest = Test{:,3:end}; [xtestnorm] = mapminmax('apply', xtest',xopt); [ytestnorm] = mapminmax('apply',ytest',yopt); xtest = xtest'; for i = 1:length(ytestnorm)-k Test_xNorm{i} = reshape(xtestnorm(:,i:i+k-1),6,1,1,k); Test_yNorm(i) = ytestnorm(i+k-1); Test_y(i) = ytest(i+k-1); end Test_yNorm = Test_yNorm'; %% % BiLSTM 层设置，参数设置 inputSize = size(Train_xNorm{1},1); %数据输入x的特征维度 outputSize = 1; %数据输出y的维度 numhidden_units1=50; numhidden_units2= 20; numhidden_units3=100; opts = trainingOptions('adam', ... 'MaxEpochs',10, ... 'GradientThreshold',1,... 'ExecutionEnvironment','cpu',... 'InitialLearnRate',0.001, ... 'LearnRateSchedule','piecewise', ... 'LearnRateDropPeriod',2, ... %2个epoch后学习率更新 'LearnRateDropFactor',0.5, ... 'Shuffle','once',... % 时间序列长度 'SequenceLength',k,... 'MiniBatchSize',24,... 'Verbose',0); %% LSTM layers = [ ... sequenceInputLayer([inputSize,1,1],'name','input') %输入层设置 sequenceFoldingLayer('name','fold') convolution2dLayer([2,1],10,'Stride',[1,1],'name','conv1') batchNormalizationLayer('name','batchnorm1') reluLayer('name','relu1') maxPooling2dLayer([1,3],'Stride',1,'Padding','same','name','maxpool') sequenceUnfoldingLayer('name','unfold') flattenLayer('name','flatten') lstmLayer(numhidden_units1,'Outputmode','sequence','name','hidden1') dropoutLayer(0.3,'name','dropout_1') bilstmLayer(numhidden_units2,'Outputmode','last','name','hidden2') dropoutLayer(0.3,'name','drdiopout_2') fullyConnectedLayer(outputSize,'name','fullconnect') % 全连接层设置（影响输出维度）（cell层出来的输出层） % tanhLayer('name','softmax') regressionLayer('name','output')]; lgraph = layerGraph(layers) lgraph = connectLayers(lgraph,'fold/miniBatchSize','unfold/miniBatchSize'); % % 网络训练 tic CNNBiLSTMNET = trainNetwork(Train_xNorm,Train_yNorm,lgraph,opts); %% 测试 close all figure Predict_Ynorm = CNNBiLSTMNET.predict(Test_xNorm); Predict_Y = mapminmax('reverse',Predict_Ynorm',yopt); Predict_Y = Predict_Y'; plot(Predict_Y,'-','Color',[0 0 1],'LineWidth',2,'Marker','s','MarkerSize',5,'MarkerFaceColor',[0 0 1]) hold on plot(Test_y,'-','Color',[0.5 0.5 0.5],'LineWidth',2,'Marker','o','MarkerSize',5,'MarkerFaceColor',[0.5 0.5 0.5]); legend('预测值','实际值') figure hold on plot(Predict_Y(10:110),'gs--','LineWidth',2.0) plot(Test_y(10:110),'ko--','LineWidth',2.0) legend('CNN-LSTM预测值','真实值') title('测试') % 预测结果评价 ae= abs(Predict_Y - Test_y); rmse = (mean(ae.^2)).^0.5; mse = mean(ae.^2); mae = mean(ae); mape = mean(ae./Predict_Y); disp('机组预测结果评价指标：') disp(['RMSE = ', num2str(rmse)]) disp(['MSE = ', num2str(mse)]) disp(['MAE = ', num2str(mae)]) disp(['MAPE = ', num2str(mape)])