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A management orientated approach to combination forecasting of tourism demand

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  • Coshall, John T.
  • Charlesworth, Richard

Abstract

Volatility, exponential smoothing, regression and Naïve 2 models are considered singly and in combination in terms of forecasting demand for international tourism. These models generate accurate predictions of tourism flows, but their prime utility is when combined with other models. Usually, models are combined by means of purely statistical criteria. We show that goal programming (GP) offers an alternative, flexible approach to model combination. GP offers planners a practical solution to tourism forecasting problems, since the method is more adaptable than conventional minimisation of prediction error, by permitting practitioners to prioritise a series of management related goals. Forecasters can focus on longer- and short-term goals, minimising forecast under- and over-estimation and/or concentrate on prediction errors in tourism flows at various times of the year.

Suggested Citation

  • Coshall, John T. & Charlesworth, Richard, 2011. "A management orientated approach to combination forecasting of tourism demand," Tourism Management, Elsevier, vol. 32(4), pages 759-769.
    • Handle: RePEc:eee:touman:v:32:y:2011:i:4:p:759-769
    DOI: 10.1016/j.tourman.2010.06.011
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    3. Xi Wu & Adam Blake, 2023. "Does the combination of models with different explanatory variables improve tourism demand forecasting performance?," Tourism Economics, , vol. 29(8), pages 2032-2056, December.
    4. Li, Cheng & Ge, Peng & Liu, Zhusheng & Zheng, Weimin, 2020. "Forecasting tourist arrivals using denoising and potential factors," Annals of Tourism Research, Elsevier, vol. 83(C).
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    6. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    7. Komkrit Wongkhae & Songsak Sriboonchitta & Kanchana Choketaworn & Chukiat Chaiboonsri, 2012. "Does price matter? The FMOLS and DOLS estimation of industrial countries tourists outbound to four ASEAN countries," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 1(4), pages 107-128, December.
    8. Nicholas Apergis & Andrea Mervar & James E. Payne, 2017. "Forecasting disaggregated tourist arrivals in Croatia," Tourism Economics, , vol. 23(1), pages 78-98, February.
    9. Yang, Yang & Fan, Yawen & Jiang, Lan & Liu, Xiaohui, 2022. "Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?," Annals of Tourism Research, Elsevier, vol. 93(C).
    10. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    11. Narayan, Paresh Kumar & Sharma, Susan Sunila & Bannigidadmath, Deepa, 2013. "Does tourism predict macroeconomic performance in Pacific Island countries?," Economic Modelling, Elsevier, vol. 33(C), pages 780-786.
    12. Ji Wu & Xian Cheng & Stephen Shaoyi Liao, 2020. "Tourism forecast combination using the stochastic frontier analysis technique," Tourism Economics, , vol. 26(7), pages 1086-1107, November.
    13. K.B. Nowman & S. Van Dellen, 2012. "Forecasting Overseas Visitors to the UK Using Continuous Time and Autoregressive Fractional Integrated Moving Average Models with Discrete Data," Tourism Economics, , vol. 18(4), pages 835-844, August.
    14. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    15. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.
    16. Shaolong Suna & Dan Bi & Ju-e Guo & Shouyang Wang, 2020. "Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach," Papers 2002.08021, arXiv.org, revised Mar 2020.
    17. Jennifer C. H. MIN & Hsien-Hung KUNG & Tsangyao CHANG, 2019. "Testing the Structural Break of Taiwan Inbound Tourism Markets," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 117-130, June.
    18. Shobande Olatunji Abdul & Shodipe Oladimeji Tomiwa, 2020. "Re-Evaluation of World Population Figures: Politics and Forecasting Mechanics," Economics and Business, Sciendo, vol. 34(1), pages 104-125, February.

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