## 4 Results and Discussion
### Limitations and Feature Works  
In this study, while the effectiveness of the model for stock price prediction was validated through various methods and experiments, several limitations remain and directions  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Loss Function** & **Precision** & **Recall** & **Accuracy** & \(\mathbf{R^{2}}\) &
\begin{tabular}{c} **Adjusted** \\ \(\mathbf{R^{2}}\) \\ \end{tabular} \\ \hline Mean Square Error Loss [29] & 0.89 & 0.87 & 0.89 & 0.91 & 0.91 \\ Huber Loss [30] & 0.82 & 0.79 & 0.81 & 0.83 & 0.83 \\ Log Cosh Loss [31] & 0.92 & 0.91 & 0.91 & 0.93 & 0.93 \\ Long-Distance Loss & 0.95 & 0.91 & 0.94 & 0.97 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different loss functions on the evaluation metrics.  
for further research exist. Firstly, concerning model selection, although the adopted models demonstrated commendable performance in stock price prediction, there may be other potential models or algorithms that could be suitable for this task or might outperform the current ones. For instance, Random Forest and Support Vector Machines could exhibit superior performance in certain time series forecasting tasks. Furthermore, regarding attention mechanisms, although various mechanisms were incorporated into the model and their effectiveness was verified through ablation studies, other attention patterns or variations might confer greater advantages to the model. In terms of the loss function, the long-distance loss function indeed showcased promising results in experiments; however, there might be other loss functions or combinations that could provide more benefits for this task. Additionally, although an attempt was made to partition the dataset based on the sequence of the time series, other partitioning strategies might have a more profound impact on the model's generalization capabilities. From a mathematical perspective, even though mathematical theoretical support was provided for various methods and models, other mathematical or statistical principles might better elucidate the performance of the models. Such potential principles could guide further improvements in the model.  
For future endeavors, further exploration and optimization of these models or other contemporary machine learning models for stock price prediction tasks can be considered. Especially for time series forecasting tasks, a deeper investigation into other attention patterns or their variations is warranted. Contemplating further research and refinement of the Long-Distance Loss function or exploring other potential loss function combinations also present a promising avenue. Moreover, employing more advanced data augmentation techniques, such as adversarial training or semi-supervised learning, could further enhance the generalization and robustness of the model. In conclusion, although progress was achieved in stock price prediction tasks in this research, ample exploration space remains in model selection, attention mechanisms, and loss function design. It is hoped that future research can further advance the field, offering more insights and innovative methodologies for time series forecasting.