Setia Wardani

Comparative Analysis of Statistical and Machine Learning Models for Air Passenger Demand Forecasting: Evidence from Domestic and International Passenger Segments

This study benchmarks four forecasting approaches—SARIMA, SARIMAX, Prophet, and XGBoost—on monthly domestic and international passenger traffic data from 2019 to 2024, using a 12-month hold-out test set and a 36-month projection horizon, revealing stark segmentation in model suitability.