## 5 Concluding Remarks
In particular, proactively preparing for periods when a sharp increase in VIX is anticipated can help minimize potential losses stemming from market shocks, while maximizing portfolio returns. Moreover, the integration of the Attention mechanism enables the model to more effectively capture \"critical time points\" and \"key features\" within the market--signals that conventional LSTM models or simple signal decomposition approaches might miss--thereby enhancing predictive precision. As a result, this framework holds promise for broad applicability and heightened practical value across various real-world scenarios, such as trading and risk monitoring systems. Furthermore, the model proposed in this study can be applied to various fields beyond finance. For instance, in the healthcare sector, machine learning (ML) models facilitate more effective and timely interventions by predicting disease outbreaks, patient outcomes, and treatment responses [40; 41]. Additionally, the proposed model can be utilized in predicting farmland flooding by integrating ML models with weather forecasts [42]. Despite its strong predictive capabilities, the proposed model has several limitations. The high computational cost associated with VMD and hyperparameter tuning makes it less suitable for real-time applications or large-scale datasets. Moreover, the model's performance heavily depends on the quality of input data and may remain sensitive to noise and abrupt market fluctuations. Although VMD contributes to noise reduction, additional measures are required to further enhance robustness. The inclusion of both VMD and the Attention mechanism also increases architectural complexity, which reduces interpretability and makes it challenging to analyze feature importance or fully understand the model's internal decision-making process. To address these limitations, future work may explore alternative signal decomposition methods with lower computational overhead and consider simplifying the architecture to improve interpretability and deployment potential. In terms of efficiency, the basic training and testing of the proposed model were completed within approximately 19 s on an NVIDIA RTX 2060 Super GPU, highlighting its practicality despite the use of Cascaded LSTM and Attention modules. When extended evaluation procedures--such as bootstrap-based confidence interval estimation, rolling-window backtesting, and residual diagnostics--were included, the total runtime increased to approximately 127 s. Nonetheless, this level of computational demand remains manageable and suggests that the model is feasible for deployment in time-sensitive applications, including real-time risk monitoring or automated trading systems. These results collectively demonstrate that the proposed architecture strikes a favorable balance between predictive performance and computational efficiency. Although the proposed model demonstrated superior forecasting performance compared to all benchmark models, several practical and methodological considerations should be addressed. First, while rolling-window evaluations were employed to assess temporal generalization, the analysis did not explicitly segment performance under distinct market regimes (e.g., high vs. low volatility). Future research could incorporate scenario-specific testing to identify performance boundaries more precisely. Second, the hybrid architecture--combining VMD, Cascaded LSTM layers, and Multi-Head Attention--yields considerable gains in accuracy but at the cost of increased model complexity. This complexity may challenge interpretability and require careful tuning, particularly for practitioners with limited computational resources. In such cases, simplified architectures or modular variants of the model may offer more practical alternatives with acceptable performance trade-offs. For practitioners, the model shows promise for near real-time applications, such as automated trading or dynamic risk monitoring, particularly when GPU acceleration is available.