## 4 Experimental Results and Analysis
### Comparative Analysis of Experimental Results
#### 4.4.2 Ablation Experiments  
Figure 5 shows the ablation experiment point-line diagrams, which mainly show the comparison of the prediction performance indexes among various models under different module combinations. This Figure clearly shows the performance differences in the models with different module combinations in key indicators such as Loss Value, MAE, MSE, RMSE, MAPE, and R-Squared in the form of dotted lines. Through these visual point-line displays, we can intuitively observe the advantages and disadvantages of each model in different performance dimensions, which helps us to deeply analyze the influence of different modules on the prediction performance of the overall model, so as to provide valuable reference and intuitive decision support for the optimization and improvement of the model and its rational application in related application scenarios. Table 2 shows a comparison of predictive performance indicators in different module combinations in ablation experiments, including the use of CNN, BiLSTM, and GRU modules, as well as key evaluation indicators such as Loss Value, MAE, MSE, RMSE, MAPE, R-Squared, training time, and prediction time. The following comprises a detailed  
explanation of the results of each indicator and a discussion of the reliability and ability of the model. CNN model: The Loss Value is 0.0163, indicating that the average difference between the predicted value and the true value of the model is small, but there is still room for improvement; the MAE value is 0.0963; this reflects the average level of prediction error and is an intuitive error measurement standard. The RMSE value is 0.1265, considering the square of the error, which is more sensitive to the larger error, and provides another evaluation of the prediction accuracy. The MAPE value is 31.88%, indicating the percentage of the prediction error relative to the true value, which is an important index in the evaluation of the prediction accuracy. The R-Squared value is 0.7757; a value close to 1 indicates that the model has a strong ability to fit the data, but there is still room for improvement. The training time is 42.97 s, which is relatively short and suitable for fast iterations and experiments. The prediction time value is 0.0925 s, indicating that the model can quickly make predictions in practical applications; as the basic model, the CNN model shows a basic performance in the prediction task. Although its performance is acceptable, there is still room for improvement by integrating other modules. CNN-BiLSTM model: After adding a BiLSTM module to the CNN, the performance of the model is significantly improved. Loss Value, MAE, RMSE, and other indicators have decreased, indicating that the prediction error is reduced, and the prediction accuracy is improved. The significant decrease in the MAPE value further confirms this. The R-Squared value is close to 1, indicating that the model has a good fitting effect on the data. However, \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**CNN** & **BiLSTM** & **GRU** & **Loss** & **MAE** & **MSE** & **RMSE** & **MAPE** & **R-Squared** & **Training** & **Prediction** \\  & & & **Value** & & & & & & & **Time/s** & **Time/s** \\ \hline \(\surd\) & \(\times\) & \(\times\) & 0.0163 & 0.0963 & 0.0163 & 0.1265 & 31.88 & 0.7757 & **42.97** & **0.0925** \\ \(\surd\) & \(\surd\) & \(\times\) & 0.0162 & 0.0959 & 0.0161 & 0.1259 & 29.93 & 0.7777 & 167.31 & 0.2684 \\ \(\surd\) & \(\surd\) & \(\surd\) & **0.0160** & **0.0947** & **0.0160** & **0.1255** & **18.40** & **0.7788** & 125.04 & 0.1882 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of predictive performance indexes in different module combination ablation experiments. Figure 5: Experimental point-line diagrams for ablation. since the BiLSTM module increases the complexity of the model, the training time and prediction time increase significantly. Nevertheless, the improvement of the model in terms of accuracy and fitting ability is still worthy of recognition.