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Temporal pattern-aware temperature forecasting using CatBoost: A hybrid machine learning approach
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DOI：10.1016/j.rineng.2026.110212.png)
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Abstract
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• Introduced an Adaptive Temporal Drift Encoding (ATDE) mechanism that dynamically models seasonal variability for improved temporal awareness. • Developed a robust and interpretable CatBoost-based forecasting framework optimized through Optuna adaptive hyperparameter search. • Demonstrated strong cross-regional and cross-country generalization across mountainous, coastal, rainforest, and continental climates. • Conducted detailed extreme-weather and high-error analyses to evaluate model robustness under volatile climatic conditions. • Quantified the contribution of temporal feature engineering through an ablation study, showing a 21.6 % MSE reduction and R² improvement of +0.07.
Keywords:
Temperature forecasting
Time series cross-validation
CatBoost
Temporal feature engineering
Ordered boosting, model generalization
Extreme weather prediction
Meteorological data
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