The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Tourism Combining Demand Forecasting Models for the Tourism in Korea

관광 수요를 위한 결합 예측 모형에 대한 연구

  • Son, H.G. (Department of Applied Statistics, Chung-Ang University) ;
  • Ha, M.H. (Department of Applied Statistics, Chung-Ang University) ;
  • Kim, S. (Department of Applied Statistics, Chung-Ang University)
  • 손흥구 (중앙대학교 응용통계학과) ;
  • 하명호 (중앙대학교 응용통계학과) ;
  • 김삼용 (중앙대학교 응용통계학과)
  • Received : 2012.02.27
  • Accepted : 2012.03.28
  • Published : 2012.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper applies forecasting models such as ARIMA, Holt-Winters and AR-GARCH models to analyze daily tourism data in Korea. To evaluate the performance of the models, we need single and double seasonal models that compare the RMSE and SE for a better accuracy of the forecasting models based on Armstrong (2001).

본 논문은 일별 관광수요 자료를 분석하기 위하여 시계열의 대표적인 3개 모형인 ARIMA, Holt-Winters, AR-GARCH 모형을 적용하였다. 모형의 성능을 비교하기 위해 Armstrong (2001)이 제안한 방법을 이용하여 서로 다른 방법의 예측값을 단순결합과 MSE, SE를 이용한 결합법을 이용하여 정확도 높일 수 있음을 확인하였다.

Keywords

References

  1. 윤지성, 허남균, 김삼용, 허희영 (2010). 계절형 다변량 시계열 모형을 이용한 국제항공 여객 및 화물 수요예측에 관한 연구, <한국통계학회 논문집>, 17, 473-481. https://doi.org/10.5351/CKSS.2010.17.3.473
  2. 허남균, 정재윤, 김삼용 (2009). 다변량 시계열 모형을 이용한 항공 수요 예측 연구, <응용통계연구>, 22, 1007-1077. https://doi.org/10.5351/KJAS.2009.22.5.1007
  3. Armstrong, J. (2001). Combining forecasting, International Series in Operations Research and Management Science, 417-440.
  4. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  5. Box, G. E. P. and Jenkins, G. (1994). Time Series Analysis: Forecasting and Control, Prentice Hall.
  6. Caiado, J. (2010). Performance of combined double seasonal univariate time series models for forecasting water demand, Journal of Hydrologic Engineering, 15, 215-222. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000182
  7. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the Econometric Society, 987-1007.
  8. Shen, S., Li, G. and Song, H. (2009). Effect of seasonality treatment on the forecasting performance of tourism demand models, Tourism Economics, 15, 693-708. https://doi.org/10.5367/000000009789955116
  9. Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing, Journal of the Operational Research Society, 54, 799-805. https://doi.org/10.1057/palgrave.jors.2601589
  10. Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting, European Journal of Operational Research, 204, 139-152. https://doi.org/10.1016/j.ejor.2009.10.003

Cited by

  1. Performance Evaluation of Time Series Models using Short-Term Air Passenger Data vol.25, pp.6, 2012, https://doi.org/10.5351/KJAS.2012.25.6.917