基于长短期神经网络LSTM的沪深300指数预测

clc clear close all %读取double格式数据 [num1,ax,ay ]= xlsread('沪深300指数.csv',1); num=[]; n1 = length(num1); num = []; for ii = 1:n1-1 num = [num ; [num1(ii,:) num1(ii+1,1)]]; end m = round(0.6*length(num)); n = 1:length(num); input_train =num(n(1:m),1:9);%训练数据输出数据 output_train = num(n(1:m),10);%训练数据输入数据 input_test = num(m+1:end,1:9);%测试数据输出数据 output_test = num(m+1:end,10);%测试数据输入数据 [inputn,inputps]=mapminmax(input_train',-1,1);%训练数据的输入数据的归一化 [outputn,outputps]=mapminmax(output_train',-1,1);%训练数据的输出数据的归一化de inputn_test=mapminmax('apply',input_test',inputps); %% Define Network Architecture % Define the network architecture. numFeatures = size(num(:,1:9),2);%输入层维度 numResponses = size(num(:,end),2);%输出维度 % 200 hidden units numHiddenUnits = 70;%第一层维度 % a fully connected layer of size 50 & a dropout layer with dropout probability 0.5 layers = [ ... sequenceInputLayer(numFeatures)%输入层 lstmLayer(numHiddenUnits,'OutputMode','sequence')%第一层 fullyConnectedLayer(60)%链接层 dropoutLayer(0.5)%遗忘层 fullyConnectedLayer(numResponses)%链接层 regressionLayer];%回归层 % Specify the training options. % Train for 60 epochs with mini-batches of size 20 using the solver 'adam' maxEpochs = 60;%最大迭代次数 miniBatchSize = 30;%最小批量 % the learning rate == 0.01 % set the gradient threshold to 1 % set 'Shuffle' to 'never' options = trainingOptions('adam', ... %解算器 'MaxEpochs',maxEpochs, ... %最大迭代次数 'MiniBatchSize',miniBatchSize, ... %最小批次 'InitialLearnRate',0.01, ... %初始学习率 'GradientThreshold',inf, ... %梯度阈值 'Shuffle','every-epoch', ... %打乱顺序 'Plots','training-progress',... %画图 'Verbose',0); %不输出训练过程 %% Train the Network net = trainNetwork(inputn,outputn,layers,options);%开始训练 %% Test the Network y_pred = predict(net,inputn_test,'MiniBatchSize',1)';%测试仿真输出 y_pred=(mapminmax('reverse',y_pred,outputps))'; %% figure%打开一个图像窗口 plot(y_pred,'k-*')%黑色实线，点的形状为* hold on%继续画图 plot(output_test,'r-o')%红色实线，点的形状为o hold off%停止画图 title('测试图')%标题 ylabel('y')%Y轴名称 legend('测试值','实际值')%标签 error1 = y_pred'-output_test;%误差 figure plot(error1,'k-*') title('测试误差图') ylabel('误差') [MSE,RMSE,MBE,MAE ] =MSE_RMSE_MBE_MAE(output_test',y_pred) R2 = R_2(output_test',y_pred)