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http://dx.doi.org/10.14400/JDC.2020.18.12.087

Air passenger demand forecasting for the Incheon airport using time series models  

Lee, Jihoon (Department of Computer Science and Statistics, Daegu University)
Han, Hyerim (Department of Computer Science and Statistics, Daegu University)
Yoon, Sanghoo (Department of Computer Science and Statistics, Daegu University)
Publication Information
Journal of Digital Convergence / v.18, no.12, 2020 , pp. 87-95 More about this Journal
Abstract
The Incheon airport is a gateway to and from the Republic of Korea and has a great influence on the image of the country. Therefore, it is necessary to predict the number of airport passengers in the long term in order to maintain the quality of service at the airport. In this study, we compared the predictive performance of various time series models to predict the air passenger demand at Incheon Airport. From 2002 to 2019, passenger data include trend and seasonality. We considered the naive method, decomposition method, exponential smoothing method, SARIMA, PROPHET. In order to compare the capacity and number of passengers at Incheon Airport in the future, the short-term, mid-term, and long-term was forecasted by time series models. For the short-term forecast, the exponential smoothing model, which weighted the recent data, was excellent, and the number of annual users in 2020 will be about 73.5 million. For the medium-term forecast, the SARIMA model considering stationarity was excellent, and the annual number of air passengers in 2022 will be around 79.8 million. The PROPHET model was excellent for long-term prediction and the annual number of passengers is expected to be about 99.0 million in 2024.
Keywords
Incheon Airport; Time series model; Exponential smoothing; SARIMA; PROPHET;
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