DOI QR코드

DOI QR Code

A study on demand forecasting for Jeju-bound tourists by travel purpose using seasonal ARIMA-Intervention model

계절형 ARIMA-Intervention 모형을 이용한 여행목적 별 제주 관광객 수 예측에 관한 연구

  • Song, Junmo (Department of Computer Science and Statistics, Jeju National University)
  • 송준모 (제주대학교 전산통계학과)
  • Received : 2016.04.20
  • Accepted : 2016.05.21
  • Published : 2016.05.31

Abstract

This study analyzes the number of Jeju-bound tourists according to travellers' purposes. We classify the travellers' purposes into three categories: "Rest and Sightseeing", "Leisure and Sport", and "Conference and Business". To see an impact of MERS outbreak occurred in May 2015 on the number of tourists, we fit seasonal ARIMA-Intervention model to the monthly arrivals data from January 2005 to March 2016. The estimation results show that the number of tourists for "Leisure and Sport" and "Conference and Business" were significantly affected by MERS outbreak whereas arrivals for "Rest and Sightseeing" were little influenced. Using the fitted models, we predict the number of Jeju-bound tourists.

본 연구에서는 제주를 방문하는 관광객 수를 여행목적 별로 분석하였다. 여행목적은 "휴양 및 관람", "레저 및 스포츠", 그리고 "회의 및 업무"를 위한 여행으로 구분되어 있으며, 2005년 1월부터 2016년 3월까지 자료를 이용하였다. 2015년 5월에 발생한 메르스 (MERS, 중동호흡기증후군) 사태의 영향을 반영하기 위하여 계절형 ARIMA-Intervention 모형을 이용한 개입분석을 수행하였다. 분석결과 메르스사태는 "레저 및 스포츠"와 "회의 및 업무"를 목적으로하는 관광객 수에 6월 한 달간 영향을 끼친 것으로 나타났으며, 이로 인하여 이 기간 동안 30%에서 40% 정도의 관광객이 감소한 것으로 추정되었다. 반면, "휴양 및 관람"에서는 메르스사태의 영향이 유의하지 않은 것으로 나타났다. 본 결과를 토대로 향후 1년의 월별 관광수요를 예측하여 보았다.

Keywords

References

  1. Box, G. E. P. and Tiao, G. C. (1975). Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association, 70, 70-79. https://doi.org/10.1080/01621459.1975.10480264
  2. Cho, S., Sohn, Y. and Seong, B. (2016). Time series analysis, Yulgok book publishing Co., Seoul.
  3. Choi, K. and Kim, J. (2001). A study on forecasting of overseas tour - Gravity model and regression model. Journal of the Korean Data & Information Science Society, 12, 103-111.
  4. Cryer, J. D. and Chan, K. S. (2008). Time series analysis: With applications in R, Springer-Verlag, New York.
  5. Han, G. H., Jung, J. and Yoo, J. K. (2014). A study on prediction for attendances of Korean pro baseball games using covariates. Journal of the Korean Data & Information Science Society, 25, 1481-1489. https://doi.org/10.7465/jkdi.2014.25.6.1481
  6. Huh, H. J. and Kim, H. C. (2001). Forecasting demand for Jeju-bound tourist: An application of intervention method. Journal of Tourism Sciences, 25, 27-42.
  7. Kim, S. and Lee, J. H. (2011). A Study on the seasonal effects of the tourism demand forecasting models. Korean Journal of Applied Statistics, 24, 93-102. https://doi.org/10.5351/KJAS.2011.24.1.093
  8. Kim, S. and Seong, B. (2011). Intervention analysis of Korea tourism data. Korean Journal of Applied Statistics, 24, 735-743. https://doi.org/10.5351/KJAS.2011.24.5.735
  9. Lee, C. K, Song, H. J and Mjelde J.W. (2008). The forecasting of international Expo tourism using quantitative and qualitative techniques. Tourism Management, 29, 1084-1098. https://doi.org/10.1016/j.tourman.2008.02.007
  10. Phillips, P. C. B. and Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75, 335-346. https://doi.org/10.1093/biomet/75.2.335
  11. Ryu, S. R. and Kim, J. T. (2013). Time series regression model for forecasting the number of elementary school teachers. Journal of the Korean Data & Information Science Society, 24, 321-332. https://doi.org/10.7465/jkdi.2013.24.2.321
  12. Shin, Y. and Yoon, S. (2016). Electricity forecasting model using specific time zone. Journal of the Korean Data & Information Science Society, 27, 275-284. https://doi.org/10.7465/jkdi.2016.27.2.275
  13. Song, D. Y. (2015). A study on forecasting the number of tourist in Jeju island focusing on travel purposes and types with seasonal ARIMA models, Master Thesis, Jeju national university, Jeju.
  14. Song, H. and Li, G. (2008). Tourism demand modelling and forecasting - A review of recent research. Tourism Management, 29, 203-220. https://doi.org/10.1016/j.tourman.2007.07.016

Cited by

  1. 계절형 ARIMA-Intervention 모형을 이용한 한국 편의점 최적 매출예측 vol.14, pp.11, 2016, https://doi.org/10.15722/jds.14.11.201611.83
  2. 개선된 유전자 역전파 신경망에 기반한 예측 알고리즘 vol.28, pp.6, 2017, https://doi.org/10.7465/jkdi.2017.28.6.1327
  3. The Study on the Tourism Demand Characteristics and Forecasting of Jeju Island vol.43, pp.4, 2016, https://doi.org/10.32780/ktidoi.2018.43.4.111
  4. 다중개입 계절형 ARIMA 모형을 이용한 KTX 수송수요 예측 vol.32, pp.1, 2019, https://doi.org/10.5351/kjas.2019.32.1.139
  5. Visitor arrivals forecasts amid COVID-19: A perspective from the Europe team vol.88, pp.None, 2021, https://doi.org/10.1016/j.annals.2021.103182
  6. The impact of the Middle East Respiratory Syndrome coronavirus on inbound tourism in South Korea toward sustainable tourism vol.29, pp.7, 2016, https://doi.org/10.1080/09669582.2020.1797057