한국항공운항학회지 (Journal of the Korean Society for Aviation and Aeronautics)

  • 제31권1호
  • /
  • Pages.92-98
  • /
  • 2023
  • /
  • 1225-9705(pISSN)
  • /
  • 2466-1791(eISSN)
한국항공운항학회 (The Korean Society for Aviation and Aeronautics)
권호 표지
DOI QR코드

DOI QR Code

ARIMA 시계열 모형을 이용한 제주도 인바운드 항공여객 증가율 예측 연구 - 제주지역 골프장 내장객 현황 데이터를 활용하여 -

Estimating the Growth Rate of Inbound Air Travelers to Jeju with ARIMA Time-Series - Using Golf Course Visitor Data -

  • 손건희 (주식회사 미예) ;
  • 김기웅 (한국항공대학교 경영학과) ;
  • 신리현 (한국항공대학교 경영학과) ;
  • 이수미 (한국항공협회 항공연구실)
  • 투고 : 2023.02.10
  • 심사 : 2023.02.24
  • 발행 : 2023.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This paper used the golf course visitors' data in Jeju region to forecast the growth of inbound air traveler to Jeju. This is because the golf course visitors were proven to bring the highest economic and production inducement effect to the Jeju region. Based on such a data, this paper forecast the short-term growth rate of inbound air traveler using ARIMA to the Jeju until December 2025. According to ARIMA (0,1,0) (0,1,1) model, it was analyzed that the monthly number of golf course visitors to Jeju has been increasing steadily even since COVID-19 pandemic and the number is expected to grow until the end of 2025. Applying the same parameters of ARIMA (0,1,0) (0,1,1) to inbound air travel data, it was found the growth rate of inbound air travelers would be higher than the growth rate of 2019 shortly without moderate variation even though the monthly number of inbound travelers to Jeju had been dropped during COVID-19 pandemic.

키워드

참고문헌

  1. Kang, D. Y., Min, S. H., and Park, S. K. "The Effect of COVID-19 Pandemic on Korean Economy and Industries", KIET Industry and Economics, Special Edition, 2021, pp.7-20. 
  2. Choi, Y. G., "The Sustainable Reactivation of Golf Tourism after COVID-19 Pandemic", Jeju Research Institute, 2020, pp.64-73. 
  3. Abonazel, M. R., and Abd-Elftah, A. I., "Fore- casting Egyptian GDP using ARIMA models", Reports on Economics and Finance, 5, 2019, pp.35-47.  https://doi.org/10.12988/ref.2019.81023
  4. Kim, M. S., Kim, K. W., and Park, S. S., "A study on the air travel demand forecasting using time series ARIMA-intervention model", Journal of Korean Society for Aviation and Aeronautics, 20(1), 2012, pp.66-75.  https://doi.org/10.12985/ksaa.2012.20.1.063
  5. Kwon, T. Y., "Future prediction of visit to Jeju using time series analysis with ARIMA", M.S. Thesis, Big Data Specialist Dept., The Graduate School of NamSeoul University, 2019. 
  6. Libal, U., and Johansson, K. H., "Yule-walker equations using higher order statistics for nonlinear autoregressive model", International Journal of Stochastic Analysis, 151823, 2011, pp.1-20. 
  7. Yao, Q., and Brockwell, P. J. "Gaussian maximum likelihood estimation for ARMA models II: Spatial processes", Bernoulli, 12(3), 2006, pp.403-429.  https://doi.org/10.3150/bj/1151525128
  8. Box, G. E. P., and Jenkins, G. M., "Time Series Analysis: Forecasting and Control", 2nd ed. San Francisco: Holden-Day, 1976. 
  9. Box, G. E. P., Jenkins, G. M., and Reinsel G. C., "Time Series Anlaysis: Forecasting and Control", 3rd ed. New Jersey: Prentice Hall, 1994. 
  10. Yoon, H. Y., and Park, S. S., "Analysis and estimation of food and beverage sales at Incheon Int'l airport by ARIMA-intervention time series model", Journal of Korea Academia-Industrial Cooperation Society, 20(2), 2019, pp.458-468. 
  11. Hanke, J. E., and Wichern, D. W., "Business Forecasting 9th Pearson International Ed.", Pearson/Prentice Hall, 2009. 
  12. Lewis, C. D., "International and Business Forecasting Methods", Butterworths, London, 1982. 
  13. Cheng, M. H., Wu, Y. C., and Chen, M. C., "Television meets Facebook: The correlation between TV ratings and social media", American Journal of Industrial and Business Management, 6(3), 2016, pp.282-290.  https://doi.org/10.4236/ajibm.2016.63026
  14. Ljung, G., and Box, G. C., "On a measure of lack of fit in time series models", Biometrica, 65, 1978, pp.265-270.  https://doi.org/10.2307/2335207
  15. Moffat, I., and Akpan, E., "White noise analysis: A measure of time series model adequacy", Applied Mathematics, 10, 2019, pp.989-1003.  https://doi.org/10.4236/am.2019.1011069