Matlab实现LSTM和BiLSTM时间序列预测对比（完整程序和数据）

clc; clear; close all; %% 导入数据 load DATA; % 导入60天的负荷数据 figure(1); plot(fuhe); legend('电力负荷'); xlabel('时间(15min)'); ylabel('功率(Kw)'); title('60日总负荷数据'); %% 数据归一化处理 % 归一化到0-1之间 Temp = fuhe; [~,PS] = mapminmax(fuhe,0,1); Temp = mapminmax('apply',Temp,PS); %% 输入输出构建及滑动时间窗输入结构构建 %数据分组 Windows = 8; %时间窗长度 T = 96; %预测未来一天(15min一次) %各个输入集合大小 X_Train = cell(size(Temp,2)-T-Windows,1); % 训练数据 Y_Train = cell(size(Temp,2)-T-Windows,1); % 测试数据 X_Test = cell(T,1); Y_Test = cell(T,1); % 训练数据构建 for i = 1:size(Temp,2)-T-Windows X_Train{i} = Temp(:,i:i+Windows-1)'; Y_Train{i} = Temp(:,i+Windows); end % 测试数据构建 for i = 1:T X_Test{i} = Temp(:,end-T+i-Windows:end-T+i-1)'; Y_Test{i} = Temp(:,end-T+i); end %% 定义神经网络 numFeatures = Windows; % 输入特征个数(等于滑动窗口大小) numResponses = 1; % 输出特征个数 numHiddenUnits = 80; % 隐含层神经元个数 Train_number = 120; % 训练次数 dorp_rate = 0.2; % 遗忘率(可以防止过拟合，0.1表示遗忘30%) [layer_lstm,layer_bilstm,options] = Net_definition(numFeatures,numResponses,numHiddenUnits,Train_number,dorp_rate); %网络定义函数 %% 网络训练 net_lstm = trainNetwork(X_Train,Y_Train,layer_lstm,options); net_bilstm = trainNetwork(X_Train,Y_Train,layer_bilstm,options); %% 网络测试 YPred_lstm = predict(net_lstm,X_Test); %lstm预测 YPred_bilstm = predict(net_bilstm,X_Test); %bi-lstm预测 % 反归一化 True = []; Predict_lstm = []; Predict_bilstm = []; for i = 1:T True = [True,mapminmax('reverse',Y_Test{i},PS)]; Predict_lstm = [Predict_lstm,mapminmax('reverse',YPred_lstm{i},PS)]; Predict_bilstm = [Predict_bilstm,mapminmax('reverse',YPred_bilstm{i},PS)]; end Predict_lstm = double(Predict_lstm); Predict_bilstm = double(Predict_bilstm); %% 误差分析 RMSE_lstm = sqrt(mean((True-Predict_lstm).^2)); RMSE_bilstm = sqrt(mean((True-Predict_bilstm).^2)); fprintf('LSTM的预测误差为：'); disp(RMSE_lstm); fprintf('Bi-LSTM的预测误差为：'); disp(RMSE_bilstm); %% 结果绘制 figure(2); plot(True,'r-'); hold on plot(Predict_lstm,'b-'); plot(Predict_bilstm,'g-'); hold off legend('真实值','lstm预测','bi-lstm预测'); xlabel("时间(15min)"); ylabel("功率(Kw)"); title("一日内负荷预测情况"); figure(3) subplot(2,1,1) plot(True) hold on plot(Predict_lstm,'.-') hold off legend(["真实值" "LSTM预测值"]) ylabel("Cases") title("LSTM预测误差图像") subplot(2,1,2) stem(True - Predict_lstm) xlabel("时间") ylabel("误差") title("RMSE = " + RMSE_lstm) figure(4) subplot(2,1,1) plot(True) hold on plot(Predict_bilstm,'.-') hold off legend(["真实值" "Bi-LSTM预测值"]) ylabel("Cases") title("Bi-LSTM预测误差图像") subplot(2,1,2) stem(True - Predict_bilstm) xlabel("时间") ylabel("误差") title("RMSE = " + RMSE_bilstm)