【Transformer回归预测】基于EMD-Transformer实现多变量交通数据回归预测附matlab代码 (2).rar

%% GRU多变量回归预测 %% 加载数据与数据集划分 clc;clear;close all data = xlsread('data.xlsx', 'Sheet1', 'A3:M1250'); % 输入数据 input =data(:,1:12)'; output=data(:,13)'; nwhole =size(data,1); % 打乱数据集 % temp=randperm(nwhole); % 不打乱数据集 temp=1:nwhole; train_ratio=0.9; ntrain=round(nwhole*train_ratio); ntest =nwhole-ntrain; % 准备输入和输出训练数据 input_train =input(:,temp(1:ntrain)); output_train=output(:,temp(1:ntrain)); % 准备测试数据 input_test =input(:, temp(ntrain+1:ntrain+ntest)); output_test=output(:,temp(ntrain+1:ntrain+ntest)); T_train=output_train; T_test=output_test; %% 数据归一化 method=@mapminmax; [inputn_train,inputps]=method(input_train); inputn_test=method('apply',input_test,inputps); [outputn_train,outputps]=method(output_train); outputn_test=method('apply',output_test,outputps); % 创建元胞或向量，长度为训练集大小； XrTrain = cell(size(inputn_train,2),1); YrTrain = zeros(size(outputn_train,2),1); for i=1:size(inputn_train,2) XrTrain{i,1} = inputn_train(:,i); YrTrain(i,1) = outputn_train(:,i); end % 创建元胞或向量，长度为测试集大小； XrTest = cell(size(inputn_test,2),1); YrTest = zeros(size(outputn_test,2),1); for i=1:size(input_test,2) XrTest{i,1} = inputn_test(:,i); YrTest(i,1) = outputn_test(:,i); end M=size(inputn_train,2); N=size(inputn_test,2); %% 创建混合GRU网络架构 % 输入特征维度 numFeatures = size(inputn_train,1); % 输出特征维度 numResponses = 1; FiltZise = 10; % 创建"-GRU"模型 layers = [... % 输入特征 sequenceInputLayer([numFeatures 1 1],'Name','input') sequenceFoldingLayer('Name','fold') % 特征提取 % convolution2dLayer([FiltZise 1],32,'Padding','same','WeightsInitializer','he','Name','conv','DilationFactor',1); % batchNormalizationLayer('Name','bn') % eluLayer('Name','elu') % averagePooling2dLayer(1,'Stride',FiltZise,'Name','pool1') % 展开层 sequenceUnfoldingLayer('Name','unfold') % 平滑层 flattenLayer('Name','flatten') % GRU特征学习 gruLayer(128,'Name','GRU1','RecurrentWeightsInitializer','He','InputWeightsInitializer','He') % GRU输出 gruLayer(32,'OutputMode',"last",'Name','bil4','RecurrentWeightsInitializer','He','InputWeightsInitializer','He') dropoutLayer(0.25,'Name','drop3') % 全连接层 fullyConnectedLayer(numResponses,'Name','fc') regressionLayer('Name','output') ]; layers = layerGraph(layers); layers = connectLayers(layers,'fold/miniBatchSize','unfold/miniBatchSize'); %%GRU训练选项 % 批处理样本 MiniBatchSize =24; % 最大迭代次数 MaxEpochs = 60; % 学习率 learningrate = 0.005; % 一些参数调整 if gpuDeviceCount>0 mydevice = 'gpu'; else mydevice = 'cpu'; end options = trainingOptions( 'adam', ... 'MaxEpochs',100, ... 'GradientThreshold',1, ... 'InitialLearnRate',learningrate, ... 'LearnRateSchedule','piecewise', ... 'LearnRateDropPeriod',20, ... 'LearnRateDropFactor',0.8, ... 'L2Regularization',1e-3,... 'Verbose',false, ... 'ExecutionEnvironment',mydevice); %% 训练混合网络 % rng(0); %% 训练模型 % 权重初始化 D = ones(1, M) / M; %% 参数设置 K = 10; % 弱回归器个数 H = 6; % 隐藏层节点个数 %% 弱回归器回归 for i = 1 : K %% 训练网络 net = trainNetwork(XrTrain,YrTrain, layers , options); %% 仿真预测 t_sim1{i} = predict(net, XrTrain); t_sim2{i} = predict(net,XrTest ); %% 数据格式转换 t_sim1{i} = double(t_sim1{i}); t_sim2{i} = double(t_sim2{i}); %% 数据反归一化 T_sim11 = mapminmax('reverse', t_sim1{i}', outputps); T_sim22 = mapminmax('reverse', t_sim2{i}', outputps); %% 均方根误差 error1(i,:) = T_sim11 - T_train; %% 调整D值 weight(i) = 0; for j = 1 : M if abs(error1(i, j)) > 0.1 weight(i) = weight(i) + D(i, j); D(i + 1, j) = D(i, j) * 1.1; else D(i + 1, j) = D(i, j); end end %% 弱分类器i权重 weight(i) = 0.5 / exp(abs(weight(i))); %% D值归一化 D(i + 1, :) = D(i + 1, :) / sum(D(i + 1, :)); end %% 强预测器预测 weight = weight / sum(weight); %% 强分类器分类结果 T_sim1 = zeros(M,1); T_sim2 = zeros(N,1); for i = 1 : K output1 = (weight(i) * t_sim1{i}); output2 = (weight(i) * t_sim2{i}); T_sim1 = output1 + T_sim1; T_sim2 = output2 + T_sim2; end %% 数据反归一化 T_sim1 = mapminmax('reverse', T_sim1', outputps); T_sim2 = mapminmax('reverse', T_sim2', outputps); %% 均方根误差 error1 = sqrt(sum((T_sim1 - T_train).^2) ./ M); error2 = sqrt(sum((T_sim2 - T_test ).^2) ./ N); %% 相关指标计算 % R2 R1 = 1 - norm(T_train - T_sim1)^2 / norm(T_train - mean(T_train))^2; R2 = 1 - norm(T_test - T_sim2)^2 / norm(T_test - mean(T_test ))^2; disp(['训练集数据的R2为：', num2str(R1)]) disp(['测试集数据的R2为：', num2str(R2)]) % 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(['测试集数据的MAE为：', num2str(mae2)]) %% 平均绝对百分比误差MAPE MAPE1 = mean(abs((T_train - T_sim1)./T_train)); MAPE2 = mean(abs((T_test - T_sim2)./T_test)); disp(['训练集数据的MAPE为：', num2str(MAPE1)]) disp(['测试集数据的MAPE为：', num2str(MAPE2)]) % MBE mbe1 = sum(T_sim1 - T_train) ./ M ; mbe2 = sum(T_sim2 - T_test ) ./ N ; disp(['训练集数据的MBE为：', num2str(mbe1)]) disp(['测试集数据的MBE为：', num2str(mbe2)]) %均方误差 MSE mse1 = sum((T_sim1 - T_train).^2)./M; mse2 = sum((T_sim2 - T_test).^2)./N; disp(['训练集数据的MSE为：', num2str(mse1)]) disp(['测试集数据的MSE为：', num2str(mse2)]) [R1_Transformer,rmse1_Transformer,biaozhuncha1_Transformer,mae1_Transformer,mape1_Transformer]=calc_error(T_train,T_sim1);%训练集 [R2_Transformer,rmse2_Transformer,biaozhuncha2_Transformer,mae2_Transformer,mape2_Transformer]=calc_error(T_test,T_sim2);%测试集 %% 绘图 figure plot(T_sim1,'-p','Color',[255 0 0]./255,'linewidth',1,'Markersize',2,'MarkerFaceColor','b') hold on plot(T_train,'-^','Color',[50 50 50]./255,'linewidth',1,'Markersize',2,'MarkerFaceColor','r') legend('预测值','真实值') xlabel('预测样本') ylabel('预测结果') string = {'训练集预测结果对比'; ['RMSE=' num2str(error1)]}; title(string) xlim([1, M]) grid ax=gca;hold on % ------------------------------------------------------------------------- % 坐标区域修饰 ax.XLabel.FontWeight='bold'; ax.YLabel.FontWeight='bold'; % 设置轴线粗细和刻度朝外 ax.LineWidth=1; ax.TickDir='out'; %% 训练集误差图 figure bar((T_sim1 - T_train)./T_train) legend('模型训练集相对误差','Location','NorthEast','FontName','楷体') title('模型训练集相对误差','fontsize',12,'FontName','楷体') ylabel('误差','fontsize',12,'FontName','楷体') xlabel('样本','fontsize',12,'FontName','楷体') xlim([1 M]); figure plot(T_sim2,'-p','Color',[255 0 0]./255,'linewidth',1,'Markersize',2,'MarkerFaceColor','b') hold on plot(T_test,'-^','Color',[50 50 50]./255,'linewidth',1,'Markersize',2,'MarkerFaceColor','r') legend('预测值','真实值') xlabel('预测样本') ylabel('预测结果') string = {'测试集预测结果对比'; ['RMSE=' num2str(error2)]}; title(string) xlim([1, N]) grid ax=gca;hold on % ------------------------------------------------------------------------- % 坐标区域修饰 ax.XLabel.FontW