An Adaptively Weighted XGBoost-SVR Ensemble Model for Civil Aircraft Demand Prediction

Accurate forecasting of future demand for civil aircraft is of great significance for formulating development strategies, allocating resources and planning future development in the aviation industry. However, civil aircraft demand is influenced by multiple complex nonlinear factors such as macroeconomic conditions and air transport volume, making it difficult for traditional single prediction models to fully capture its inherent regularities. In order to improve prediction accuracy, this paper proposes an adaptive weighting combination prediction model based on XGBoost and SVR. Historical data from 2013 to 2024 with multiple factors are utilized as predictive indicators and Bayesian optimization is employed to tune the hyperparameters of the XGBoost and SVR models separately. An adaptive weighting mechanism is then applied to combine the two prediction models, enabling accurate prediction of future civil aircraft demand. Experimental results show that, compared to standalone XGBoost or SVR models, the proposed model significantly reduces prediction errors and improves all evaluation metrics, fully demonstrating its effectiveness and superiority in civil aircraft demand prediction.