## 4 Case Study
### Results Analysis  
According to the comparative analysis of the models' performance in the table, it can be seen that the WACNN and CNN models perform optimally in terms of prediction accuracy, with the WACNN having the highest R\({}^{2}\) value of 0.99973, as well as the lowest \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Train Set** & **Test Set** \\ \hline Group 1 & 1–4 years & The fifth year \\ Group 2 & 2–5 years & The sixth year \\ Group 3 & 3–6 years & The seventh year \\ Group 4 & 4–7 years & The eighth year \\ Group 5 & 5–8 years & The ninth year \\ Group 6 & 6–9 years & The tenth year \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Cross-validation partitioning strategy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **R\({}^{2}\)** & **MBE** & **MAPE** & **RMSE** & **Promotion Rate** & **MAE** & **Promotion Rate** \\ \hline WACNN & 0.99973 & 0.1194 & 0.0081 & 15.596 & — & 11.8649 & — \\ CNN & 0.99972 & \(-\)2.0177 & 0.0079 & 16.3521 & 4.62\% & 12.1138 & 2.05\% \\ LSTM & 0.99502 & 11.436 & 0.0335 & 69.3158 & 77.5\% & 51.7759 & 77.08\% \\ BiLSTM & 0.99458 & 1.6356 & 0.0378 & 71.7462 & 78.26\% & 53.2867 & 77.73\% \\ GRU & 0.99646 & \(-\)9.8724 & 0.0319 & 61.0531 & 74.46\% & 45.3181 & 73.82\% \\ RF & 0.99892 & 1.0652 & 0.0085 & 29.9312 & 47.89\% & 14.0925 & 15.81\% \\ SVR & 0.99913 & 0.1067 & 0.0175 & 28.6886 & 45.64\% & 26.1449 & 54.62\% \\ PLS & 0.91261 & 10.0184 & 0.1597 & 286.2715 & 94.55\% & 228.0626 & 94.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of model prediction results. RMSE and MAE of 15.596 and 11.8649, respectively, with an MBE of 0.1194 and a MAPE as low as 0.0081%, which indicates that the prediction results are close to the true values and have high robustness. The ML models RF and SVR have relatively good performance with a higher prediction accuracy. It is worth noting that the LSTM, BiLSTM, and GRU models have larger prediction errors, with a MAPE over 3%, reflecting that the temporal models may have had insufficient feature capture for this task. The worst performing model is PLS, with a test set RMSE of 286.2715 and a prediction accuracy of only 84%. Compared with the other ML and DL models, there is a significant gap, proving that the performance of these models is indeed better than that of traditional statistical models. The excellent prediction results of the WACNN and CNN models are due to their convolutional structure that can effectively extract local features and capture spatial correlations in the data, while the WACNN further optimizes the feature learning process by introducing a WA. Unlike traditional CNNs that treat all input data equally, WACNNs focuses models more on the key information that is strongly related to the prediction target because the input data are given different weights. Because in time series or spatial data, certain local segments may contain more significant patterns or trends, the WA mechanism enhances the differentiation of feature representations by increasing the expression weight of these high-value features while weakening the interference of noisy or unimportant regions. In contrast, traditional CNNs lack this explicit weight regulation ability, which may lead to insufficient attention to key features or overfitting of redundant information. Therefore, WACNNs achieve more refined feature filtering and fusion through weight allocation to enhance features. However, the poor prediction performance of the three time series prediction models is mainly due to the mismatch between their model structure and task characteristics. The core advantage of these three models is their capturing of long-term temporal dependencies, but the input data are relatively large and there are complex nonlinear concerns between the different indicator data, making it difficult for these models to effectively extract valuable information. The RF and SVR, as traditional machine learning models, perform well on this regression prediction problem.