基于BP神经网络的时间序列预测-matlab程序

%% 清空环境变量 warning off % 关闭报警信息 close all % 关闭开启的图窗 clear % 清空变量 clc % 清空命令行 %% 导入数据（时间序列的单列数据） result = xlsread('数据集.xlsx'); %% 数据分析 num_samples = length(result); % 样本个数 kim = 15; % 延时步长（kim个历史数据作为自变量） zim = 1; % 跨zim个时间点进行预测 %% 构造数据集 for i = 1: num_samples - kim - zim + 1 res(i, :) = [reshape(result(i: i + kim - 1), 1, kim), result(i + kim + zim - 1)]; end %% 划分训练集和测试集 temp = 1: 1: 922; P_train = res(temp(1: 700), 1: 15)'; T_train = res(temp(1: 700), 16)'; M = size(P_train, 2); P_test = res(temp(701: end), 1: 15)'; T_test = res(temp(701: end), 16)'; N = size(P_test, 2); %% 数据归一化 [p_train, ps_input] = mapminmax(P_train, 0, 1); p_test = mapminmax('apply', P_test, ps_input); [t_train, ps_output] = mapminmax(T_train, 0, 1); t_test = mapminmax('apply', T_test, ps_output); %% 创建网络 net = newff(p_train, t_train, 5); %% 设置训练参数 net.trainParam.epochs = 1000; % 迭代次数 net.trainParam.goal = 1e-6; % 误差阈值 net.trainParam.lr = 0.01; % 学习率 %% 训练网络 net= train(net, p_train, t_train); %% 仿真测试 t_sim1 = sim(net, p_train); t_sim2 = sim(net, p_test); %% 数据反归一化 T_sim1 = mapminmax('reverse', t_sim1, ps_output); T_sim2 = mapminmax('reverse', t_sim2, ps_output); %% 均方根误差 error1 = sqrt(sum((T_sim1 - T_train).^2) ./ M); error2 = sqrt(sum((T_sim2 - T_test ).^2) ./ N); %% 绘图 figure plot(1: M, T_train, 'r-', 1: M, T_sim1, 'b-', 'LineWidth', 1) legend('真实值','预测值') 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 figure plot(1: N, T_test, 'r-', 1: N, T_sim2, 'b-', 'LineWidth', 1) legend('真实值','预测值') 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 %% 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)]) % 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)])