Forecasting air passenger traffic volume : evaluating time series models in long-term forecasting of Kuwait air passenger data
Al-Sultan, Ahmad and Al-Rubkhi, Amani and Alsaber, Ahmad and Pan, Jiazhu (2021) Forecasting air passenger traffic volume : evaluating time series models in long-term forecasting of Kuwait air passenger data. Advances and Applications in Statistics, 70 (1). pp. 69-89. ISSN 0972-3617 (https://doi.org/10.17654/AS070010069)
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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.
ORCID iDs
Al-Sultan, Ahmad, Al-Rubkhi, Amani, Alsaber, Ahmad ORCID: https://orcid.org/0000-0001-9478-0404 and Pan, Jiazhu ORCID: https://orcid.org/0000-0001-7346-2052;-
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Item type: Article ID code: 77478 Dates: DateEvent1 September 2021Published2 August 2021AcceptedSubjects: Science > Mathematics Department: Faculty of Science > Mathematics and Statistics Depositing user: Pure Administrator Date deposited: 19 Aug 2021 11:02 Last modified: 03 Dec 2024 01:22 URI: https://strathprints.strath.ac.uk/id/eprint/77478