## 4 Results and Discussion
### Limitations and Future Work  
Despite the significant performance improvements achieved by the proposed sliding-window attention mechanism and sliding loss function in multimodal time-series and spatial data processing tasks, certain limitations require further exploration. Firstly, the proposed method exhibits sensitivity to specific hyperparameters, such as window size, sliding stride, and loss function weights. These hyperparameters must be manually adjusted according to the dataset and task objectives, potentially increasing the adaptation cost in diverse application scenarios. Developing an adaptive parameter optimization mechanism that enables the model to automatically tune hyperparameters based on the data distribution represents an important direction for future research. In practical applications, the correlations within multimodal data often exhibit higher complexity, and simple attention mechanisms may fall short of fully capturing their latent characteristics. Future studies could explore the integration of more advanced multimodal feature fusion techniques, such as multimodal contrastive learning or cross-modal generative adversarial networks, to enhance the model's comprehensive ability to represent multimodal features. Although the proposed method outperforms traditional global attention mechanisms in computational  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** & **FPS** \\ \hline Time-Series—None & 0.63 & 0.60 & 0.62 & 0.61 & 28 \\ Time-Series—Synthetic Data & 0.74 & 0.71 & 0.73 & 0.73 & 34 \\ Time-Series—Adversarial Samples & 0.88 & 0.83 & 0.85 & 0.84 & 37 \\ Time-Series—Proposed Method & 0.95 & 0.91 & 0.93 & 0.93 & 46 \\ Spatial Data—None & 0.65 & 0.68 & 0.66 & 0.67 & 31 \\ Spatial Data—Synthetic Data & 0.73 & 0.70 & 0.72 & 0.71 & 38 \\ Spatial Data—Adversarial Samples & 0.85 & 0.81 & 0.83 & 0.82 & 42 \\ Spatial Data—Proposed Method & 0.93 & 0.90 & 0.92 & 0.91 & 49 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation of model robustness under synthetic data and adversarial samples.  
efficiency, training on large-scale datasets remains time-consuming, particularly when the number of sliding windows is substantial. To address this issue, future research could incorporate more efficient model compression techniques or hardware acceleration methods, such as optimizing attention mechanisms using low-rank decomposition or quantizing network weights. These approaches may further reduce computational complexity and resource consumption, paving the way for broader applicability in large-scale scenarios. Future research will explore the applicability of the proposed framework in a broader range of multimodal prediction tasks. Beyond financial forecasting, autonomous driving, and medical imaging, this approach also holds potential value in climate modeling, network security, and industrial IoT applications. Climate data exhibit high-dimensional spatiotemporal dependencies, where the sliding-window attention mechanism can effectively capture both local and global variations, improving long-term trend predictions. In network security, time-series log data and evolving attack patterns require dynamic adaptation, and the proposed method enhances anomaly detection capabilities and improves robustness in attack identification. For industrial IoT applications, sensor fusion is a critical challenge, and the localized computation of the sliding loss function facilitates sensor data alignment and feature extraction. Further experimental studies will be conducted in these domains to evaluate the generalization capability of the framework and optimize its computational efficiency and predictive accuracy across different data patterns.