Abstract
Accurate estimation of air transport demand is vital for airlines, related aviation companies, and government agencies. For example, both short-term and long-term business plans of airlines require accurate forecasting of future air traffic flows. This study aims to forecast the volume of air passengers in Kuwait International Airport (KIA), which is in the state of Kuwait. Using monthly air traffic volume data between January 2012 and December 2018, this study focuses on the modelling and forecasting the number of air passengers in KIA. A wide range of time series forecasting models are considered in this research, including autoregressive-integrated-moving average model (ARIMA), exponential smoothing with errors term (ETS), Holt-Winters exponential smoothing, neural network autoregression (NNAR), hybrid and Bayesian structural time series (BSTS), and a hybrid model. The forecasting performance of these models are compared using multiple train-test splits where the models are fitted on the training sets and evaluated on the test sets. The mean absolute percentage error (MAPE) is used to compare the performance of various models. Empirical analysis suggests that the BSTS model compares favorably against the other time series models in its ability to forecast complex time series. The BSTS model may be applied to study other complex time series forecasting problems with irregularity.
Highlights
Reliable forecast of civil aviation activity is critical in the planning process of states, airports, airlines, and other relevant organizations
Work Strategic and tactical decisions of both the airport and airline company management depend on accurate forecast of air passenger traffic flows
This study concludes that the Bayesian structural time series model achieved superior forecasting accuracy and was selected as the candidate model to predict the air traffic flow from 2019 to 2023
Summary
Reliable forecast of civil aviation activity is critical in the planning process of states, airports, airlines, and other relevant organizations. Accurate forecasts of traffic flow are necessary for airline companies to optimally allocate their financial resources, to adapt their flight frequencies, and to adjust their price policy. Various attempts have been made over the past decades to forecast the traffic flows in various airports around the globe (Grubb and Mason [9], BaFail [5], Al-Rukaibi and Al-Mutairi [3], Bougas [6] and Xie et al [17]). Time series forecasting involves taking models fit on historical data and using them to predict future observations. A wide range of time series models have been applied to forecast the number of air passengers, including the autoregressive
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