Enhanced tourist flow forecasting in Aosta Valley: A novel ensemble AI framework with adaptive temporal dynamics
Fig 5
Adaptive weight distribution across different weather conditions.
During adverse weather (heavy snow, storms), the ensemble increases Random Forest weight from 20% to 35% due to its robustness to outliers and missing data, simultaneously reducing XGBoost weight from 45% to 30%. This automatic rebalancing occurs because Random Forest’s bagging mechanism provides inherent stability against data perturbations, while XGBoost’s gradient-based optimization becomes less reliable when weather disruptions create non-representative training patterns. Conversely, during clear weather periods with stable traffic patterns, XGBoost dominates (45% weight) as its ability to model complex feature interactions proves most effective for regular forecasting scenarios.