## 4 Experimental Results and Analysis
### Quantitative Analyses
\\ \cline{1-1} \cline{2-4}  & A hybrid model combining CNN for feature extraction & CNN output dimension: 128, XGBoost parameters: number of trees: 200, learning rate: 0.001, learning rate: 0.001, epochs: 50, batch size: 128, and seed: 666. \\ \cline{1-1} \cline{2-4}  & A hybrid model combining CNN for feature extraction & CNN output dimension: 128, XGBoost parameters: number of trees: 200, learning rate: 0.001, learning rate: 0.001, learning rate: 0.  
comparison models (e.g., CNN is 0.0720, LSTM is as high as 0.2427), indicating that the MDFFE model has a significant advantage in mean absolute error and high prediction accuracy. In terms of RMSE, the MDFFE model's 0.0084 is also much lower than other models (e.g., CNN's 0.0838, LSTM's 0.3332), reflecting its strong stability. On the MAPE index, the MDFFE model is only 0.0071. After eliminating the influence of dimension, the relative error is very small and the control ability is outstanding. The R-Squared of the MDFFE model is as high as 0.9970, which indicates that the fitting effect of the model on the data is almost perfect. In contrast, the R-Squared value of other models is much lower than that of the MDFFE model, and even negative (such as \(-0.6273\) of CNNLSTM and \(-3.7164\) of LSTM), which indicates that they have a large deficiency in data fitting. From the perspective of error square sum SSE, the MDFFE model is only 1.5090, which is much lower than other models (such as 206.7034 of BiLSTModel and 150.8885 of CNN), which once again confirms the excellent prediction performance of the MDFFE model. Figure 5 shows the point-line diagram of the ablation experiment, including the performance of MAE, RMSE, MAPE, R-Squared, and SSE on different model configurations (CNN, CNN-FE, and MDFFE). On the MAE, RMSE, and MAPE indicators, we observed a significant downward trend in the corresponding points from CNN to CNN-FE to MDFFE. Figure 4: The experimental point line diagram (shows the performance comparison of different models on multiple evaluation indicators, including MAE, RMSE, MAPE, R-Squared, and SSE). This intuitively shows that with the addition of FE and XGBoost-integrated components, the prediction error of the model is gradually reduced, and the prediction accuracy is continuously improved. On the R-Squared index, CNN has the lowest corresponding point, CNN-FE increased, and MDFFE has the highest, which indicates that R-Squared increases significantly with model optimization, confirming the great contribution of feature engineering and model fusion to model fitting ability. In terms of SSE index, CNN has the highest point, CNN-FE decreases, and MDFFE is the lowest, indicating that SSE continues to decrease with the improvement of the model, further verifying the effective reduction in the cumulative error of the model. Table 4 shows the quantitative analysis results of ablation under different model settings. The key elements involved include the use of CNN, FE, and XGBoost, and the important evaluation index data of MAE\(\downarrow\), RMSE\(\downarrow\), MAPE\(\downarrow\), R-Squared, and SSE \(\downarrow\) are presented. The best results are highlighted in bold. Table 4 clearly shows that as the model components are gradually added, the performance indicators are significantly improved. When CNN is only enabled, MAE is 0.0720, RMSE is 0.0838, MAPE is 0.0720, R-Squared is 0.7019, and SSE is 150.8885. When CNN was enabled and FE was added, the indicators were greatly improved: MAE decreased to 0.0250, RMSE decreased to 0.0376, MAPE was 0.0254, R-Squared increased to 0.9353, and SSE decreased to 44.6601. This fully demonstrates that feature engineering plays a key role in extracting effective information and improving model performance. When the complete MDFFE model (including CNN, FE, and XGBoost) is enabled, the indicators reach the best, MAE is as low as 0.0068, RMSE is 0.0084, MAPE is 0.0071, R-Squared is as high as 0.9970, and SSE is only 1.5090.