Abstract
Modeling uncertainty is a key issue in forecasting. In the tourism area, forecasts are used by governments, airline companies and operators to design tourism policies and they should include a quantification of uncertainties. This paper proposed a new approach to forecast the tourism demand, which is time series models combined with belief functions. We used this method to predict the demand for China international tourism, with an explicit representation of forecast uncertainty. The monthly data of international tourist arrival cover the period from January 1991 to June 2013. The result show that time series models combined with belief functions is a computationally simple and effective method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Although the founding of New China is in 1949, the ban on inbound travel for any purpose was enforced between 1949 and 1976. Since Deng Xiaoping’s economic reforms in 1978, inbound tourism in China rapidly developed due to change in this policy (Lim and Pan [11]).
- 2.
According to the likelihood principle, \( L_x (\phi ) \) is the likelihood function defined by \( L_x (\phi )=\xi f_\phi (x) \), for all \(\phi \in \varPsi \), where \( \xi \) is any positive multiplicative constant. On the basis of compatibility with Bayesian inference, the contour function \( pl_x (\phi ) \) associated to \( Bel_x^\varPsi \) should be proportional to the likelihood function: \( pl_x (\phi )\propto L_x (\varphi ) \). More details can be found in Denoeux (2014).
- 3.
Total tourist arrivals are all those traveling China on non-Chinese passports, include holders of Hong Kong, Macau and Republic of China (Taiwan) passports and travel documents.
- 4.
Training data (insample data) is form January 1991 to June 2012. The remaining period from July 2013 to June 2013 are testing data (out-of-sample data).
- 5.
The QQ plots of error term are available on request.
- 6.
The MAPE and RMSE are defined as:
\({\mathrm {MAPE}} = \dfrac{1}{K}\sum \limits _{t = N + 1}^{N + K} \left| {\frac{{{{\hat{y}}_t} - {y_t}}}{{{y_t}}}} \right| \times 100,{\mathrm {RSME}} = \sqrt{\dfrac{1}{K}\sum \limits _{t = N + 1}^{N + K} {{\left( {{{\hat{y}}_t} - {y_t}} \right) }^2}}\).
References
Chang, C.L., Sriboonchitta, S., Wiboonpongse, A.: Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation. Math. Comput. Simul. 79, 1730–1744 (2009)
Cho, V.: A comparison of three different approaches to tourist arrival forecasting. Tour. Manag. 24, 323–330 (2003)
Chu, F.L.: A fractionally integrated autoregressive moving average approach to forecasting tourism demand. Tour. Manag. 29, 79–88 (2008)
Chu, F.L.: Forecasting tourism demand with ARMA-based methods. Tour. Manag. 30, 740–751 (2009)
Claveria, C., Torra, S.: Forecasting tourism demand to Catalonia: neural networks vs. time series models. Econ. Model. 36, 220–228 (2014)
Goh, C., Law, R.: Modelling and forecasting tourism demand for arrivals with stochastic nonstationarity seasonality and intervention. Tour. Manag. 23, 499–510 (2002)
Lim, C., McAleer, M.: Time series forecasts of international travel demand for Australia. Tour. Manag. 23, 389–396 (2002)
Song, H., Witt, S.F.: Forecasting international tourist flows to Macau. Tour. Manag. 27, 214–224 (2006)
Turner, L.W., Witt, S.F.: Forecasting tourism using univariate and multivariate structural time series models. Tour. Econ. 7, 135–147 (2001)
UNWTO: UNWTO tourism highlights 2013th edn. UNWTO, Madrid (2013)
Lim, C., Pan, G.W.: Inbound tourism developments and patterns in China. Math. Comput. Simul. 68, 499–507 (2005)
Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econom. 31(3), 307–327 (1986)
Denoeux, T.: Likelihood-based belief function: justification and some extensions to low-quality data. Int. J. Approx. Reason. (2014)
Abdallah, N.B., Voyneau, N.M., Denoeux, T.: Combining statistical and expert evidence using belief functions: application to centennial sea level estimation taking into account climate change. Int. J. Approx. Reason. 55, 341–354 (2014)
Kanjanatarakul, O., Sriboonchitta, S., Denoeux, T.: Forecasting using belief functions: an application to marketing econometrics. Int. J. Approx. Reason. 55(5), 1113–1128 (2014)
Nguyen, H.T.: An Introduction to Random Sets. Chapman and Hall/CRC Press, Boca Raton (2006)
Jarque, C., Bera, A.: A test for normality of observations and regression residuals. Int Statis Rev. 55, 163–172 (1987)
Coshall, J.T.: Combining volatility and smoothing forecasts of UK demand for international tourism. Tour. Manag. 30, 495–511 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Tang, J., Sriboonchitta, S., Yuan, X. (2015). Forecasting Inbound Tourism Demand to China Using Time Series Models and Belief Functions. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S., Suriya, K. (eds) Econometrics of Risk. Studies in Computational Intelligence, vol 583. Springer, Cham. https://doi.org/10.1007/978-3-319-13449-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-13449-9_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13448-2
Online ISBN: 978-3-319-13449-9
eBook Packages: EngineeringEngineering (R0)