## 4 Dataset and Methodology
### Model Development and Feature Selection  
In this study, a comprehensive forecasting framework was developed, combining machine learning, deep learning, and statistical models with advanced feature selection techniques to enhance the predictive accuracy and efficiency of SMP forecasts in Turkey's electricity market. Given the complex and volatile nature of SMP, this framework was meticulously designed to capture both linear and non-linear relationships in the data, addressing the various interdependencies that significantly influence price fluctuations. To effectively model the SMP data, a diverse set of machine learning and deep learning models was employed. This included ELM, SVM, XGBoost, LSTM, and Bi-LSTM,each selected for its strengths in managing non-linear dynamics and high-dimensional data, which are prominent in SMP forecasting. ELM and SVM were leveraged for their high performance in pattern recognition and handling complex data structures, while XGBoost provided an effective solution for managing non-linear interactions and preventing overfitting through boosting and regularization. LSTM and Bi-LSTM were incorporated specifically to capture temporal dependencies within the SMP data, with Bi-LSTM offering a bidirectional view that accounts for both past and future price dynamics, crucial for modeling the temporal complexities present in electricity pricing. In addition to machine learning and deep learning methods, traditional statistical models such as ARIMA and SARIMA were utilized as foundational baselines. These models were chosen for their robustness in capturing clear seasonal patterns and trends within the data. Their inclusion provided a stable framework for forecasts during periods when SMP exhibits relatively consistent predictable behavior, thus balancing the more complex machine learning models in scenarios where market volatility is lower and historical patterns remain stable. Feature selection was a vital aspect of model development, significantly improving the models' predictive accuracy by narrowing down the dataset to the most impactful variables while reducing noise and multicollinearity. Two advanced feature selection techniques, mRMR and MLFS, were employed to refine the feature set systematically. The mRMR method was used to prioritize features that held high relevance to SMP while minimizing redundancy among predictors, ensuring that the selected variables were not excessively correlated, which could introduce unnecessary noise and impact predictive quality. For example, mRMR isolated specific calendar effects, such as weekends and public holidays, over more generic time indicators, allowing the model to capture unique demand patterns associated with these periods without redundant overlap from other time variables. Following the mRMR process, MLFS was applied iteratively to select features that maximized the likelihood of observed SMP outcomes. Unlike mRMR, MLFS was particularly effective in identifying non-linear relationships within the data by choosing features that made the most substantial contributions to predictive performance. This iterative approach allowed the models to adapt to complex interactions between SMP and its predictors, capturing influential macroeconomic factors, such as fluctuations in the USD/TRY exchange rate, that may not exhibit a simple linear relationship with SMP. By emphasizing features that most directly impacted SMP, MLFS further refined the dataset, enhancing both model accuracy and adaptability to Turkey's electricity market dynamics. This integrative approach, combining machine learning, deep learning, and statistical models with a robust feature selection process, resulted in a highly adaptable and accurate SMP forecasting framework. By focusing on the most relevant and distinct predictors, the framework not only maximized predictive performance across varying market conditions but also established a benchmark for SMP forecasting accuracy.