Forecasting Air Passenger Demand: A Comparison of LSTM and SARIMA

Abstract

All airports need to have an accurate prediction of the number of passengers for their efficient management. An accurate prediction of the number of air passengers is crucial task since it provides information for planning decisions in the airport infrastructure to stabilize the service and maximize the profit. This study proposes a novel air passenger demand forecasting model based on Deep Neural Network (DNN), specifically, Long Short Term Memory (LSTM) algorithm. The developed models are applied on the data from Incheon International Airport to show its effectiveness and practicability. The Seasonal Auto-Regressive Integrated Moving Average (SARIMA) method is also applied to the research problem. The performance criteria including MAPE, MSE, RMSE and MAD are used to evaluate the forecasting accuracy. The experimental results show that both SARIMA and LSTM approaches provide accurate and reliable forecasting and have greater predictive capability; however, the LSTM model shows a superior forecasting performance.