## 5 Experiments
### Comparative Experiments and Results
XGBoost starts strong with an R\({}^{2}\) of 0.652 on 1 day but sees a significant drop by 4 days (R\({}^{2}\) of 0.105). Conversely, SVR maintains a more consistent performance, demonstrating its stronger capacity to handle extended forecasts. While both models' accuracy declines over longer horizons, SVR consistently outperforms XGBoost across most metrics and steps, indicating better accuracy and robustness in handling nonlinear time series data complexities. Nevertheless, these ML models still underperform compared to DL models, particularly for longer forecasts. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Metric** & **Step Size 1** & **Step Size 2** & **Step Size 3** & **Step Size 4** \\ \hline \multirow{4}{*}{ARIMA} & R\({}^{2}\) & 0.621 & 0.128 & \(-0.292\) & \(-0.723\) \\  & MAE & 15.312 & 23.685 & 26.785 & 35.012 \\  & RMSE & 23.403 & 35.503 & 43.206 & 49.908 \\  & MAPE & 0.378 & 0.645 & 0.670 & 0.842 \\ \hline \multirow{4}{*}{XGBoost} & R\({}^{2}\) & 0.652 & 0.423 & **0.598** & 0.105 \\  & MAE & 12.944 & 18.499 & 18.336 & 23.398 \\  & RMSE & 22.588 & 29.060 & **24.316** & 36.279 \\  & MAPE & 0.258 & 0.419 & 0.446 & 0.561 \\ \hline \multirow{4}{*}{SVR} & R\({}^{2}\) & 0.660 & 0.563 & 0.550 & 0.278 \\  & MAE & 12.043 & 14.216 & 17.279 & 20.656 \\  & RMSE & 22.312 & 25.287 & 25.713 & 32.587 \\  & MAPE & 0.229 & 0.298 & 0.398 & **0.445** \\ \hline \multirow{4}{*}{RNN} & R\({}^{2}\) & 0.812 (0.008) & 0.425 (0.053) & 0.281 (0.042) & \(-0.038\) (0.044) \\  & MAE & 11.022 (0.246) & 19.158 (0.894) & 22.812 (0.761) & 30.894 (0.416) \\  & RMSE & 16.545 (0.369) & 28.870 (1.342) & 32.258 (0.960) & 38.783 (0.816) \\  & MAPE & 0.260 (0.019) & 0.538 (0.050) & 0.690 (0.044) & 0.840 (0.044) \\ \hline \multirow{4}{*}{CNN} & R\({}^{2}\) & 0.801 (0.013) & 0.644 (0.027) & 0.304 (0.071) & 0.372 (0.091) \\  & MAE & 10.565 (0.399) & 14.552 (0.698) & 19.414 (1.013) & 20.621 (0.986) \\  & RMSE & 17.006 (0.570) & 22.726 (0.884) & 31.723 (1.643) & 30.093 (2.131) \\  & MAPE & 0.221 (0.026) & 0.366 (0.033) & 0.522 (0.064) & 0.632 (0.056) \\ \hline \multirow{4}{*}{CNN-LSTM} & R\({}^{2}\) & 0.816 (0.032) & 0.644 (0.026) & 0.436 (0.044) & 0.392 (0.044) \\  & MAE & 10.670 (1.130) & 14.688 (0.952) & 19.153 (0.733) & 20.293 (0.752) \\  & RMSE & 16.284 (1.338) & 22.694 (0.842) & 28.557 (1.093) & 29.662 (1.059) \\  & MAPE & 0.212 (0.043) & 0.336 (0.032) & 0.520 (0.053) & 0.633 (0.023) \\ \hline \multirow{4}{*}{CNN-LSTM-Attention} & R\({}^{2}\) & 0.797 (0.024) & 0.588 (0.058) & 0.365 (0.047) & 0.348 (0.033) \\  & MAE & 10.420 (0.490) & 15.392 (0.640) & 19.373 (0.698) & 21.419 (0.899) \\  & RMSE & 17.170 (1.027) & 24.436 (1.671) & 30.323 (1.147) & 30.738 (0.775) \\  & MAPE & 0.201 (0.015) & 0.353 (0.018) & 0.473 (0.038) & 0.622 (0.020) \\ \hline \multirow{4}{*}{TransCNN-LSTM-Attention} & R\({}^{2}\) & 0.813 (0.024) & 0.645 (0.033) & 0.374 (0.055) & 0.360 (0.037) \\  & MAE & 10.358 (0.567) & 14.498 (0.801) & 19.029 (1.022) & 20.829 (0.491) \\ \cline{1-1}  & RMSE & 16.457 (1.040) & 22.688 (1.079) & 30.320 (1.409) & 30.434 (0.878) \\ \cline{1-1}  & MAPE & 0.198 (0.009) & 0.301 (0.030) & 0.446 (0.030) & 0.583 (0.031) \\ \hline \multirow{4}{*}{**TransTLA**} & R\({}^{2}\) & **0.826** (0.009) & **0.689** (0.025) & 0.433 (0.029) & **0.423** (0.017) \\ \cline{1-1}  & MAE & **9.396** (0.197) & **13.339** (0.605) & **17.025** (0.528) & **19.551** (0.347) \\ \cline{1-1}  & RMSE & **15.890** (0.402) & **21.231** (0.860) & 28.649 (0.734) & **28.924** (0.416) \\ \cline{1-1}  & MAPE & **0.172** (0.004) & **0.293** (0.020) & **0.396** (0.025) & 0.587 (0.024) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation results of comparative experiments. Further analysis of DL models reveals that combined models perform better than individual RNN and CNN base models. RNN shows better performance in short-term forecasts (e.g., 1-day step) but a significant drop in performance for long-term forecasts (e.g., 3 and 4 days).