XGBoost for Meteorological Forecasting
XGBoost for Meteorological Forecasting
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eXtreme Gradient Boosting (XGBoost) applied and benchmarked as the top-performing model for hourly air temperature and relative humidity forecasting.
Why It Matters
The paper specifically highlights XGBoost as the best performer, suggesting its structured comparison against deep learning models for this specific type of time series is a key finding.
Evidence
XGBoost achieves the best overall performance, with a test mean absolute error (MAE) of 0.302 °C for air temperature and 1.271% for relative humidity
Related Papers
Metadata & Links
- created_at
- 2026-03-27T15:43:39Z