Regression prediction | MATLAB implements TCN-BiLSTM temporal convolution bidirectional long-short-term memory neural network multi-input single-output regression prediction

Regression prediction | MATLAB implements TCN-BiLSTM temporal convolution bidirectional long-short-term memory neural network multi-input single-output regression prediction

predictive effect

basic introduction

1. MATLAB implements TCN-BiLSTM temporal convolution bidirectional long-term short-term memory neural network multi-input single-output regression prediction; 2. The
operating environment is Matlab2021b;
3. Input multiple features, output a single variable, and multivariate regression prediction;
4.data is a data set, excel data, the first 7 columns are input, and the last column is output. MainTCN_BiLSTMNN. evaluation
.

Model description

Since TCN has an expanded causal convolution structure and has outstanding feature extraction capabilities, the original features can be fused to obtain high-dimensional abstract features, which strengthens the mining of feature information. The BiLSTM
network has a powerful time series prediction ability. Combining the TCN and the BiLSTM network, the TCN feature extraction is input to the BiLSTM network, which improves the processing efficiency of the BiLSTM network memory unit and makes the forecasting model learn more effectively. The complex interaction relationship of time series. Therefore, this paper builds a TCN-BiLSTM prediction model.

TCN-BiLSTM is a neural network model that combines temporal convolutional neural network (TCN) and bidirectional long short-term memory neural network (BiLSTM). TCN is a convolutional neural network capable of processing sequence data, which can capture long-term dependencies in sequences. BiLSTM is a recurrent neural network with memory units that can handle both short-term and long-term dependencies in sequence data.
The input of the TCN-BiLSTM model can be multiple sequences, and each sequence can be a different feature or variable. For example, if we want to predict the average temperature of a certain city for the next week, we can use multiple variables such as temperature, humidity, air pressure, etc. in the past period of time as an input sequence. The output of the model is a value, the average temperature at some point in the future.
In TCN-BiLSTM, temporal convolutional layers are used to capture long-term dependencies in sequences, and BiLSTM layers are used to handle short-term and long-term dependencies in sequences. Multiple input sequences are merged into one tensor, which is then fed into the TCN-BiLSTM network for training. During training, the model optimization goal is to minimize the gap between the predicted output and the true value.
The TCN-BiLSTM model performs well on time series forecasting and regression problems, especially for long-term dependent sequence data. It can be used in many application scenarios, such as stock price prediction, traffic flow prediction, etc.

programming

• Complete source code and data acquisition method 1: Private message bloggers reply MATLAB to implement TCN-BiLSTM temporal convolution bidirectional long-term and short-term memory neural network multi-input single-output regression prediction ;
• Complete program and data download method 2 (subscribe to the "Combined Optimization" column, and at the same time obtain any 8 programs included in the "Combined Optimization" column, and private message me to get the data after subscription): MATLAB implements TCN-BiLSTM temporal convolution bidirectional long-term short-term memory neural network multi-input single-output regression 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