【LSTM时序预测】基于长短记忆神经网络LSTM实现交通流时间序列单步预测含验证和预测未来附matlab代码.zip

%% 1.环境清理 clear, clc, close all; %% 2.导入数据 data=xlsread('data.csv'); data1=data; % 原始数据绘图 figure plot(data,'-s','Color',[0 0 255]./255,'linewidth',1,'Markersize',5,'MarkerFaceColor',[0 0 255]./255) legend('原始数据','Location','NorthWest','FontName','华文宋体'); xlabel('样本','fontsize',12,'FontName','华文宋体'); ylabel('数值','fontsize',12,'FontName','华文宋体'); %% 3.数据处理 numTimeStepsTrain = floor(85);%85数据训练 ，7个用来验证 [XTrain,YTrain,XTest,YTest,mu,sig] = shujuchuli(data,numTimeStepsTrain); %% 4.定义LSTM结构参数 numFeatures= 1;%输入节点 numResponses = 1;%输出节点 numHiddenUnits = 500;%隐含层神经元节点数 %构建 LSTM网络 layers = [sequenceInputLayer(numFeatures) lstmLayer(numHiddenUnits) %lstm函数 dropoutLayer(0.2)%丢弃层概率 reluLayer('name','relu')% 激励函数 RELU fullyConnectedLayer(numResponses) regressionLayer]; XTrain=XTrain'; YTrain=YTrain'; %% 5.定义LSTM函数参数 def_options(); %% 6.训练LSTM网络 net = trainNetwork(XTrain,YTrain,layers,options); %% 7.建立训练模型 net = predictAndUpdateState(net,XTrain); %% 8.仿真预测(训练集) M = numel(XTrain); for i = 1:M [net,YPred_1(:,i)] = predictAndUpdateState(net,XTrain(:,i),'ExecutionEnvironment','cpu');% end T_sim1 = sig*YPred_1 + mu;%预测结果去标准化 ，恢复原来的数量级 %% 9.仿真预测(验证集) N = numel(XTest); for i = 1:N [net,YPred_2(:,i)] = predictAndUpdateState(net,XTest(:,i),'ExecutionEnvironment','cpu');% end T_sim2 = sig*YPred_2 + mu;%预测结果去标准化 ，恢复原来的数量级 %% 10.评价指标 % 均方根误差 T_train=data1(1:M)'; T_test=data1(M+1:end)'; error1 = sqrt(sum((T_sim1 - T_train).^2) ./ M); error2 = sqrt(sum((T_sim2 - T_test ).^2) ./ N); % MAE mae1 = sum(abs(T_sim1 - T_train)) ./ M ; mae2 = sum(abs(T_sim2 - T_test )) ./ N ; disp(['训练集数据的MAE为：', num2str(mae1)]) disp(['验证集数据的MAE为：', num2str(mae2)]) % MAPE maep1 = sum(abs(T_sim1 - T_train)./T_train) ./ M ; maep2 = sum(abs(T_sim2 - T_test )./T_test) ./ N ; disp(['训练集数据的MAPE为：', num2str(maep1)]) disp(['验证集数据的MAPE为：', num2str(maep2)]) % RMSE RMSE1 = sqrt(sumsqr(T_sim1 - T_train)/M); RMSE2 = sqrt(sumsqr(T_sim2 - T_test)/N); disp(['训练集数据的RMSE为：', num2str(RMSE1)]) disp(['验证集数据的RMSE为：', num2str(RMSE2)]) %% 11. 绘图 figure subplot(2,1,1) plot(T_sim1,'-s','Color',[255 0 0]./255,'linewidth',1,'Markersize',5,'MarkerFaceColor',[250 0 0]./255) hold on plot(T_train,'-o','Color',[150 150 150]./255,'linewidth',0.8,'Markersize',4,'MarkerFaceColor',[150 150 150]./255) legend( 'LSTM拟合训练数据','实际分析数据','Location','NorthWest','FontName','华文宋体'); title('LSTM模型预测结果及真实值','fontsize',12,'FontName','华文宋体') xlabel('样本','fontsize',12,'FontName','华文宋体'); ylabel('数值','fontsize',12,'FontName','华文宋体'); xlim([1 M]) %------------------------------------------------------------------------------------- subplot(2,1,2) bar((T_sim1 - T_train)./T_train) legend('LSTM模型训练集相对误差','Location','NorthEast','FontName','华文宋体') title('LSTM模型训练集相对误差','fontsize',12,'FontName','华文宋体') ylabel('误差','fontsize',12,'FontName','华文宋体') xlabel('样本','fontsize',12,'FontName','华文宋体') xlim([1 M]); %------------------------------------------------------------------------------------- figure subplot(2,1,1) plot(T_sim2,'-s','Color',[0 0 255]./255,'linewidth',1,'Markersize',5,'MarkerFaceColor',[0 0 255]./255) hold on plot(T_test,'-o','Color',[0 0 0]./255,'linewidth',0.8,'Markersize',4,'MarkerFaceColor',[0 0 0]./255) legend('LSTM预测测试数据','实际分析数据','Location','NorthWest','FontName','华文宋体'); title('LSTM模型预测结果及真实值','fontsize',12,'FontName','华文宋体') xlabel('样本','fontsize',12,'FontName','华文宋体'); ylabel('数值','fontsize',12,'FontName','华文宋体'); xlim([1 N]) %------------------------------------------------------------------------------------- subplot(2,1,2) bar((T_sim2 - T_test )./T_test) legend('LSTM模型测试集相对误差','Location','NorthEast','FontName','华文宋体') title('LSTM模型测试集相对误差','fontsize',12,'FontName','华文宋体') ylabel('误差','fontsize',12,'FontName','华文宋体') xlabel('样本','fontsize',12,'FontName','华文宋体') xlim([1 N]); %% 12.预测未来 P = 7;% 预测未来数量 YPred_3 = [];%预测结果清零 [T_sim3] = yuceweilai(net,XTrain,data,P,YPred_3,sig,mu) %% 13.绘图 figure plot(1:size(data,1),data,'-s','Color',[255 0 0]./255,'linewidth',1,'Markersize',5,'MarkerFaceColor',[250 0 0]./255) hold on plot(size(data,1)+1:size(data,1)+P,T_sim3,'-o','Color',[150 150 150]./255,'linewidth',0.8,'Markersize',4,'MarkerFaceColor',[150 150 150]./255) legend( 'LSTM预测结果','Location','NorthWest','FontName','华文宋体'); title('LSTM模型预测结果','fontsize',12,'FontName','华文宋体') xlabel('样本','fontsize',12,'FontName','华文宋体'); ylabel('数值','fontsize',12,'FontName','华文宋体');