Table 11.

Comparison of the proposed model and existing forecasting models.

Model	Learning Strategy	Bias–Variance Balance	Robustness Across Folds	Sensitivity to Data Split	Error Distribution Characteristics	Computational Demand
Gradient Boosting	Sequential tree-based boosting	Moderate bias reduction, moderate variance	Medium	Medium	Moderate spread with occasional outliers	Low
XGBoost	Regularized gradient boosting	Low bias, controlled variance	High	Low to medium	Narrow distribution with limited extremes	Medium
LightGBM	Histogram-based boosting	Low bias, moderate variance	Medium to high	Medium	Moderate distribution spread	Low to medium
CatBoost	Ordered boosting with categorical handling	Low bias, low variance	High	Low	Narrow and stable error distribution	Medium
Neural Network	Feedforward deep learning	Very low bias, high variance	Low	High	Wide distribution with higher extremes	High
LSTM	Recurrent deep learning	Very low bias, high variance	Low	High	Wide distribution with temporal instability	Very high
Proposed Stacked Regressor	Meta-ensemble of heterogeneous learners	Balanced bias and very low variance	Very high	Very low	Tight and symmetric distribution	Medium