Time series forecasting | MATLAB implements CNN-LSTM convolutional long short-term memory neural network time series forecasting (wind power forecasting)
Table of contents
predictive effect
basic introduction
1. MATLAB implements CNN-LSTM convolutional long-term short-term memory neural network time series forecasting (wind power forecasting);
2. The operating environment is Matlab2021b;
3. Single variable time series forecasting;
4.data is a data set, a single variable excel data, MainCNN_LSTMTS.m is the main program, just run it, and put all the files in one folder;
5. The command window outputs multi-index evaluations of R2, MSE, RMSE, MAE, and MAPE;
programming
- Complete source code and data acquisition method: Private letter bloggers reply to MATLAB to implement CNN-LSTM convolutional long-term short-term memory neural network time series prediction (wind power prediction) ;
%% 预测
t_sim1 = predict(net, p_train);
t_sim2 = predict(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);
%% 相关指标计算
% 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(abs(T_sim1' - T_train)) ./ M ;
mbe2 = sum(abs(T_sim1' - T_train)) ./ 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)])
References
[1] https://blog.csdn.net/kjm13182345320/article/details/128577926?spm=1001.2014.3001.5501
[2] https://blog.csdn.net/kjm13182345320/article/details/128573597?spm=1001.2014.3001.5501