## 4 Results and Discussion
Further extending the analysis, Figure 12 compares the R\({}^{2}\) values of the proposed ABSA_GRU model over the same training epochs. The model shows progressive accuracy improvements, with R\({}^{2}\) values nearing 1 for both training and validation sets. This consistent performance indicates a high reliability in fare prediction when ABSA is utilized, with R\({}^{2}\) improvements highlighting the substantial benefits of integrating passenger feedback into the predictive models. Figure 13 further shows how well the ABSA_GRU model predicts flight ticket fares. On the scatter plot, the x-axis shows the actual fares, and the y-axis displays the predicted fares. The closer the data points are to the red diagonal line, the more accurate the predictions are. It can be seen in Figure 13 that most of the model's predictions align well with the line, indicating that the model accurately predicts the flight ticket fares. The small differences from the ideal line show that the model is generally reliable across various fare ranges. The consistent efficiency of the ABSA_GRU model is evident regardless of whether the ticket fares are low or high. Its RMSE value is low, which means there is only a small difference between the predicted value and the actual fare. The performance metrics for various models are summarized in Table 7.  
Figure 11: Performance of RMSE for stacked-GRU model across seven layers. Figure 12: The proposed model of coefficient of determination R\({}^{2}\). Furthermore, a comprehensive assessment was carried out across several timeframes to ascertain the effectiveness of the GRU model with ABSA. The evaluations encompassed prediction of varying durations, ranging from short-term to long-term, and were conducted at 7, 14, 21, and 30 days. Table 8 shows the predictive accuracy of this model over these time intervals. The results in Table 8 reveal that the model demonstrates high precision in the 7-day horizon, with an RMSE of 0.0072 and an R\({}^{2}\) of 0.9769, indicating its accuracy in forecasting flight ticket fares over one week. In the 14-day timeframe, the model maintains this robustness, evidenced by an RMSE of 0.0078 and an R\({}^{2}\) of 0.9181. However, there is a noted slight decrease in accuracy for medium-term predictions. Over 21 days, the model begins to show instances of deviation, but most predictions still align closely with the actual values. The increase in RMSE to 0.0671 and the decrease in R\({}^{2}\) to 0.8246 highlights the growing impact of external and inherent uncertainties on prediction efficiency. For the interval of 30 days, the model exhibits more noticeable variations in its predictions, particularly with higher fares. The RMSE of 0.0881 and an R\({}^{2}\) of 0.8143 indicate the challenges in accurately predicting fares over a longer period. Despite the potential drop in performance over extended periods, the model still demonstrates a considerable degree of predictive capability. Figure 14 shows the model's predictive accuracy over these time intervals. \begin{table}
\begin{tabular}{l l l l} \hline
**Model** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline MLP & 0.0582 & 0.7431 & 0.6991 \\ \hline MLP with ABSA & 0.0544 & 0.7087 & 0.7644 \\ \hline LSTM & 0.0341 & 0.5989 & 0.9053 \\ \hline LSTM with ABSA & 0.0312 & 0.3684 & 0.9289 \\ \hline GRU & 0.0221 & 0.1384 & 0.9463 \\ \hline ABSA\_GRU & 0.0071 & 0.0137 & 0.9899 \\ \hline \end{tabular}
\end{table} 
Table 7: Summary of performance metrics for different models. Figure 13: Scatter plot points of actual vs. predicted flight ticket fares.