## 6 Conclusions and Future Work
### Future Work  
Despite the substantial contributions of this research, several opportunities for future work remain. One potential avenue for exploration is the development of hybrid models that integrate multiple forecasting techniques, such as combining deep learning architectures with ensemble-based approaches. Hybrid models could leverage the strengths of individual methodologies to further improve the predictive accuracy and robustness of SMP forecasts.  
Another promising direction is the application of more advanced deep learning frameworks, such as Transformers and Graph Neural Networks (GNNs). These architectures have shown exceptional performance in various domains, including natural language processing and network analysis, and could be adapted to capture complex spatiotemporal dependencies in electricity markets. The integration of these architectures with traditional models like ARIMA and SARIMA could also provide a more comprehensive approach to time series forecasting.  
In addition to methodological innovations, future research should focus on expanding the scope and quality of datasets used for SMP prediction. Incorporating additional variables related to renewable energy integration, such as solar and wind generation data, could significantly enhance the models' ability to account for fluctuations in energy supply. Furthermore, the inclusion of socio-economic factors and policy interventions could provide a more holistic view of market dynamics, leading to more accurate and context-aware forecasts.  
Another area of future work involves real-time forecasting and dynamic model updating. Developing models capable of adapting to rapidly changing market conditions and updating their predictions in real time would be invaluable for both market participants and regulators. Implementing such adaptive models would require the use of advanced reinforcement learning techniques and continuous learning frameworks, enabling the models to evolve with changing market dynamics.  
Moreover, the findings of this research suggest that feature selection plays a crucial role in improving model performance. Future studies could explore the development of new feature selection algorithms or the refinement of existing methods to identify even more relevant features for SMP forecasting. Automated feature engineering techniques, such as AutoML, could also be integrated into the model development process to streamline feature selection and hyperparameter optimization.  
Lastly, future work could extend the findings of this research to other markets and regions, testing the applicability and generalizability of the proposed models and methods in different contexts. Comparative studies across multiple electricity markets, including those with varying levels of deregulation and renewable energy integration, would provide valuable insights into the strengths and limitations of each approach, informing the development of more versatile and globally applicable SMP forecasting models.  
Conceptualization, F.A. and M.K.; methodology, F.A. and M.K.; Formal analysis, M.K.; Investigation, M.K. and M.F.A.; Project administration, M.F.A.; Supervision, M.F.A.; Writing--original draft, F.A.; Writing--review and editing, F.A. and M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data are contained within the article.  
Author [PERSON] was employed by the company SABIS Bilgi Teknololijeri ve Sanayi Ticaret Ltd. Sti. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.