## 4 Case Study
### Parameter Selection
\begin{table}
\begin{tabular}{c c c} \hline
**Layer** & **Parameters** & **Numerical Value** \\ \hline \multirow{3}{*}{Input layer} & Sample size & Training set:2800/test set:1034 \\  & Step size & [1.1] \\  & Number of features & 16 \\ \hline \multirow{6}{*}{CNN layer} & Number of convolutional layers & 5 \\  & Number of convolution kernels per layer & 16-16-32-32-64 \\  & Size of convolution kernel in each layer & [10, 1] [3, 1] [3, 1] [3, 1] [2, 1] \\ \cline{1-1}  & Convolutional layer activation function & ReLU \\ \cline{1-1}  & Convolutional layer filling method & Same padding \\ \cline{1-1}  & Number of pooling layers & 4 \\ \cline{1-1}  & Pooling layer pooling window size & [5, 1] [2, 1] [2, 1] [2, 1] \\ \hline Output layer & Number of neurons & 1 \\ \hline \end{tabular}
\end{table} 
Table 4: The overall architecture and parameter settings of the CNN model. Figure 15: SVR model parameter tuning diagram. (**a**) Punishment factor and (**b**) radial basis function. \begin{table}
\begin{tabular}{c c c c c} \hline
**Layer** & **Parameters** & **LSTM** & **BiLSTM** & **GRU** \\ \hline \multirow{2}{*}{Input layer} & Number of samples & Training set: 2800/test set: 1034 & 2800/1034 & 2800/1034 \\  & Number of neurons in the first layer & 2 & 1 & 2 \\ \cline{1-1}  & Number of neurons in the second layer & 30 & 40 & 128 \\ \cline{1-1}  & Number of neurons in the second layer & 40 & & \\ \cline{1-1}  & LSTM layer activation function & ReLU & ReLU & ReLU \\ \cline{1-1}  & Output pattern & last & last & last \\ \hline Output layer & Number of neurons & 1 & 1 & 1 \\ \hline \end{tabular}
\end{table} 
Table 5: The overall frameworks and parameter settings of the LSTM, BiLSTM, and GRU models. In the RF model, 2800 sets of data were selected as the training sets, 1034 sets of data were selected as the test sets, the number of decision trees was set to 500, and the minimum number of leaves was set to 5. In the SVR model, 2800 sets of data were selected as the training sets, 1034 sets of data were selected as the test sets, the penalization factor was set to 500, and the parameter of the radial basis function was set to 0.9. The network training method can be divided into two stages: signal forward propagation and error backpropagation. In the backpropagation process, the gradient descent method gradually approaches the value that minimizes the loss function by calculating the gradient at the current point and then updating the parameters along the opposite direction of the gradient. In this paper, the two most commonly used gradient descent algorithms, Adam and SGDM, are selected and compared. Using the constructed CNN model and the LSTM model, with both models trained 800 times, the two gradient algorithms, SGDM and Adam, are compared. The RMSE using the SGDM is 21.466, and the RMSE value using the Adam is 51.9197, from which can be seen that with the same choice of parameters, the SGDM is more effective than the Adam. In the LSTM model, the RMSE value of the Adam is 74.5857 and the RMSE value of the SGDM is 198.4812, so the SGDM is selected for the WACNN and CNN models and the Adam is selected for the LSTM, BiLSTM, and GRU models. Moreover, the hyperparameter selection of the BiLSTM and GRU is consistent with that of the LSTM. To verify the stability of the model, we conducted a hyperparameter sensitivity analysis of the proposed model. The stepwise learning rate decay schedule was selected for use. This strategy balanced the need for rapid convergence in the early stages of training with fine tuning in the later stages by reducing the learning rate in stages. Therefore, the batch size, initial learning rate, and learning rate descent factor were selected for the sensitivity analysis. Among them, a batch size that is too small may introduce more noise, while a batch size that is too large may cause the model to become stuck in local solutions or have poor generalization ability. Choosing too small an initial learning rate and learning rate  
Figure 16: WACNN model.