## 6 Conclusions and Future Work
### Conclusions  
This study focused on developing and evaluating advanced SMP forecasting models for the Turkish electricity market by leveraging traditional statistical and machine learning methods. The research aimed to overcome the limitations of traditional forecasting approaches, which often struggle with capturing non-linear dependencies and sudden shifts in market dynamics due to their linear assumptions and reliance on historical trends. By integrating sophisticated feature selection techniques, this study demonstrated that machine learning models, particularly XGBoost and ELM, consistently outperformed traditional methods like ARIMA and SARIMA under various market conditions. The results show that XGBoost, when combined with advanced feature selection methods such as MLFS and mRMR, achieved significantly lower MAPE values compared to other models, demonstrating superior robustness and predictive accuracy. The application of these feature selection techniques played a crucial role in refining the input feature set, eliminating redundant information, and focusing on the most relevant variables, thereby enhancing model performance while reducing computational complexity. These findings align with the broader literature, which suggests that the inclusion of sophisticated feature selection and hybrid approaches can significantly improve the predictive power of machine learning models. The performance of deep learning models, such as LSTM and ConvLSTM, further supports the notion that advanced models can capture complex temporal patterns and dependencies within time series data. Although these models performed well in scenarios with high volatility and long-term dependencies, their overall accuracy and consistency were still lower than XGBoost, especially on public holidays and transitional periods where sudden shifts in market conditions occur. This confirms that while deep learning models have strong potential in time series forecasting, they may struggle with overfitting and adapting to abrupt changes in data patterns, a limitation commonly noted in the literature. The date-based analysis provides further evidence of the superiority of machine learning models over traditional statistical methods. On regular weekdays and low volatility days, XGBoost models exhibited exceptional stability and predictive power, maintaining low MAPE values even without feature selection. In contrast, during high volatility periods and public holidays, where market behavior is more unpredictable, the performance of all models generally declined. However, XGBoost remained the most reliable method, consistently outperforming other approaches. This ability to adapt to diverse market conditions highlights the robustness of XGBoost and further supports its application for SMP forecasting in electricity markets, where sudden changes and non-linear dependencies are prevalent. The results of this study are consistent with broader findings in the literature, which indicate that traditional models like ARIMA and SARIMA, despite their popularity, often fail to handle the complexities of electricity markets. Their linear structure and sensitivity to non-stationary data lead to higher error rates, particularly during periods of volatility or irregular market behavior. This research highlights the limitations of traditional models and demonstrates that modern machine learning approaches, equipped with advanced feature selection techniques, can offer substantial improvements in predictive performance. The ability of XGBoost to maintain high accuracy across regular weekdays, weekends, and public holidays, while achieving MAPE values as low as 4.42% on high volatility days, underscores its effectiveness in addressing the challenges of SMP forecasting in a complex market like Turkey's. In applying advanced machine learning models like XGBoost alongside feature selection techniques (i.e., MLFS and mRMR), some limitations must be acknowledged to present a balanced view of their effectiveness in forecasting.