## 5 Results and Discussion
### Date-Based Analysis  
The date-based analysis provides an in-depth evaluation of the performance of different models on selected dates to identify patterns and assess the robustness of each approach under varying market conditions. The dates chosen for this analysis--14 February, 25 March, 19 May, 29 July, 9 August, 30 August, and 14 September--represent a range of scenarios, including regular working days, weekends, and public holidays, where sudden shifts and high volatility are expected. The performance of each model is compared based on their MAPE values, highlighting their strengths and weaknesses.  
On 14 February, Valentine's Day (Special Day), the traditional models ARIMA (18.75%) and SARIMA (18.03%) struggled. Machine learning models performed better. SVM recorded 15.52% without feature selection, improving to 14.35% with mRMR and 12.01% with MLFS. ELM also improved from 16.45% to 13.57% (mRMR) and 12.09% (MLFS). XGBoost, the top performer, achieved 7.72%, though MLFS slightly increased the MAPE to 8.91%. Among time-series-based methods, MLP showed higher error at 18.47%, while LSTM, Bi-LSTM, and ConvLSTM showed moderate performance with 13.57%, 13.04%, and 13.43%, respectively.  
On 29 July, a regular Saturday, traditional models continued to struggle, with ARIMA and SARIMA recording 8.59% and 8.96%, respectively. SVM improved from 8.72% to 9.25% with mRMR and 8.95% with MLFS. ELM showed 8.12% without selection, improving to 7.67% with mRMR and 6.97% with MLFS. XGBoost performed exceptionally well, with 5.22% without feature selection, and 4.42% with mRMR, achieving the best performance at 4.71% with MLFS. Among the time-series-based methods, MLP showed 12.00%, while LSTM performed at 8.01%, Bi-LSTM at 13.65%, and ConvLSTM at 7.12%.  
On 9 August, a regular weekday, ARIMA and SARIMA recorded 9.30% and 9.62%. SVM performed steadily, achieving 7.33% without feature selection and improving to 6.77% with mRMR and 7.05% with MLFS. ELM showed 6.84% without selection, improving to 7.67% with mRMR and 6.29% with MLFS. XGBoost was consistent with 5.08% without feature selection and 5.11% with MLFS. Among the time-series-based methods, MLP performed at 10.37%, LSTM at 9.44%, Bi-LSTM at 11.62%, and ConvLSTM at 8.25%.  
On 30 August, Victory Day (Public Holiday), traditional models performed poorly, with ARIMA at 19.91% and SARIMA at 20.72%. SVM improved with feature selection, recording 15.88% without selection, 16.25% with mRMR, and 15.92% with MLFS. ELM improved from 15.07% to 14.11% with mRMR and 14.20% with MLFS. XGBoost performed at 14.65% without feature selection, improving to 12.40% with MLFS. Among the time-series-based methods, MLP recorded 18.04%, LSTM at 16.50%, Bi-LSTM at 19.37%, and ConvLSTM at 16.89%.  
On 14 September, a regular weekday, the traditional models struggled, with ARIMA recording 19.72% and SARIMA at 19.11%. SVM performed at 14.44% without selection,improving to 13.75% with mRMR and 14.62% with MLFS. ELM improved from 12.10% to 11.17% with mRMR and 11.31% with MLFS. XGBoost maintained superior performance, recording 10.95% without selection and 10.52% with MLFS. Among the time-series-based methods, MLP showed 15.02% and LSTM performed at 18.47%, Bi-LSTM at 13.26%, and ConvLSTM at 15.28%.