## 5 Results and Discussion
### Method-Based Analysis  
The method-based analysis in this section focuses on evaluating the performance of different forecasting methods used in the study, specifically in terms of their MAPE values across various dates. The goal is to provide a comparative evaluation of each method, highlighting their strengths and limitations under different market scenarios. By doing so, this section aims to identify which models are most effective in capturing the complex dynamics of the Turkish electricity market.  
Traditional statistical models, like ARIMA and SARIMA, consistently showed higher MAPE values during the chosen dates. This shows how poorly they can handle the naturally unstable and nonlinear SMP data. For example, the ARIMA model recorded an average MAPE of 15.26%, while SARIMA had a slightly lower average of 15.19%. These high error rates suggest that traditional models are not well-suited for SMP forecasting in highly dynamic markets like Turkey's, where rapid price fluctuations and complex interdependencies between variables are prevalent. The models' reliance on historical trends and linear assumptions restricts their ability to adapt to sudden shifts in market conditions, leading to suboptimal performance.  
Machine learning models, such as SVM and ELM, outperformed traditional statistical models in most scenarios. The baseline SVM model yielded an average MAPE of 12.82%, indicating its moderate ability to capture non-linear relationships. Incorporating feature selection techniques, such as mRMR and MLFS, further improved SVM's performance. SVM combined with mRMR achieved an average MAPE of 12.27%, while SVM with MLFS further reduced the error to 12.02%. This improvement is because feature selection methods can clean up the set of features by focusing on the most useful predictors and getting rid of the rest. This lets SVM focus on the important drivers of SMP.  
ELM models, known for their rapid training time and strong generalization ability, also demonstrated robust performance in this study. The baseline ELM model achieved an average MAPE of 11.93%, but when enhanced with feature selection methods, the results improved. ELM combined with mRMR resulted in a MAPE of 10.38%, while ELM with MLFS achieved a slightly higher MAPE of 10.43%. This steady rise in performance shows that ELM, when combined with feature selection, can effectively capture the complex and  
Figure 1: Method-based average MAPE values of the models.  
nonlinear interactions found in SMP data. This makes it a strong candidate for tasks like forecasting in the electricity market.  
Among all the methods evaluated, tree-based ensemble models, particularly XGBoost, showed the most promising results. The baseline XGBoost model consistently achieved the lowest MAPE values, averaging 9.64%. The integration of feature selection techniques further enhanced XGBoost's performance, with XGBoost combined with mRMR yielding an average MAPE of 9.37% and XGBoost with MLFS achieving the lowest average MAPE of 8.68%. This highlights XGBoost's ability to handle non-linear interactions and adapt to various market conditions, making it the top-performing method for SMP forecasting.  
Deep learning models, such as LSTM, Bi-LSTM, and ConvLSTM, displayed varying levels of effectiveness depending on the market scenario. LSTM showed relatively strong performance with an average MAPE of 12.90%, while ConvLSTM performed similarly with a MAPE of 12.63%. Bi-LSTM, however, had the highest average error rate among the deep learning models, with a MAPE of 15.33%, indicating potential challenges in generalizing across datasets with irregular patterns.