Explainable AI and robust forecasting of global salary trends: Addressing data drift and unseen categories with tree-based models
This article studies salary prediction under distributional drift using explainable boosting models and hybrid forecasting. We integrate unseen-aware feature engineering, robust objectives, SHAP-based interpretability, drift detection, and time-series forecasting (Prophet/SARIMAX) on multi-year data (2020–2024), and report a comprehensive evaluation aligned with typical MMC guidelines. Modern salary data are heterogeneous, heavy-tailed, and non-stationary. Therefore we combine robust tree-based learners with drift monitoring and explainable forecasting to prioritize