基于长短期记忆网络(LSTM)对时间序列的预测分析(MATLAB)

clc; clear all; close all; warning off all; rand('seed', 10); load enso; data_x = month'; data_y = pressure'; mu = mean(data_y); sig = std(data_y); data_y = (data_y - mu) / sig; wd = 5; len = numel(data_y); wdata = []; for i = 1 : 1 : len - wd di = data_y(i:i+wd); wdata = [wdata; di]; end wdata_origin = wdata; index_list = randperm(size(wdata, 1)); ind = round(0.8*length(index_list)); train_index = index_list(1:ind); test_index = index_list(ind+1:end); train_index = sort(train_index); test_index = sort(test_index); dataTrain = wdata(train_index, :); /* ????,?????,?????????????,????????,??????,???????, ???????????,WX:Q3101759565,QQ:3101759565 */ Yall = wdata_origin(:, end)'; YPred = predict(net,Xall,'MiniBatchSize',1); rmse = mean((YPred(:)-Yall(:)).^2); figure subplot(2,1,1) plot(data_x(1:length(Yall)), Yall) hold on plot(data_x(1:length(Yall)),YPred,'.-') hold off legend(['Real' 'Predict']) ylabel('Data') title(sprintf('LSTM分析-RMSE=%.2f', rmse)); subplot(2,1,2) stem(data_x(1:length(Yall)), YPred - Yall) xlabel('Time') ylabel('Error') title('LSTM分析-误差图');