Analyzing Machine Learning Techniques for Air Passenger Numbers Forecasting

The imperative of forecasting air passenger numbers is underscored by its utility in strategic planning and operational optimization within the aviation sector. As a pivotal technique for predicting continuous data, machine learning methodologies offer a sophisticated avenue for enhancing forecast accuracy. This study embarks on a detailed examination of machine learning approaches to predict air passenger volumes in Thailand, employing a suite of models: Random Forest, XGBoost, Gradient Boosting, LightGBM and CatBoost. Comparative analysis reveals the Gradient Boosting model as the preeminent performer, demonstrating superior forecasting capabilities with an RMSE of 8,081 and a MAPE of 2.57%. These findings underscore the potential of machine learning techniques in refining the precision of air passenger number forecasts, offering significant implications for the planning and management of aviation operations.