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Non-linear models for tourism demand forecasting

Author

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  • Andrea Saayman

    (North-West University, South Africa)

  • Ilsé Botha

    (University of Johannesburg, South Africa)

Abstract

Quantitative methods for forecasting tourist arrivals can be subdivided into causal methods and non-causal methods. Non-causal time series methods remain popular tourism forecasting tools due to the accuracy of their forecasting ability and general ease of use. Since tourist arrivals exhibit seasonality, Seasonal Autoregressive Integrated Moving Average (SARIMA) models are often found to be the most accurate. However, these models assume that the time series is linear. This article compares the baseline seasonal Naïve and SARIMA forecasts of a seasonal tourist destination faced with a structural break in the data with alternative non-linear methods, with the aim of determining the accuracy of the various methods. These methods include the unobserved components model, smooth transition autoregressive model and singular spectrum analysis. The results show that the non-linear forecasts outperform the other methods. The linear methods show some superiority in short-term forecasts when there are no structural changes in the time series.

Suggested Citation

  • Andrea Saayman & Ilsé Botha, 2017. "Non-linear models for tourism demand forecasting," Tourism Economics, , vol. 23(3), pages 594-613, May.
    • Handle: RePEc:sae:toueco:v:23:y:2017:i:3:p:594-613
    DOI: 10.5367/te.2015.0532
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    References listed on IDEAS

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    4. 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.
    5. Chhorn, Theara & Chaiboonsri, Chukiat, 2017. "Modelling and Forecasting Tourist Arrivals to Cambodia: An Application of ARIMA-GARCH Approach," MPRA Paper 83942, University Library of Munich, Germany, revised 27 Dec 2017.
    6. Jean-François Verne, 2021. "Smooth Threshold Autoregressive models and Markov process: An application to the Lebanese GDP growth rate," International Econometric Review (IER), Econometric Research Association, vol. 13(3), pages 71-88, September.
    7. Yi-Chung Hu, 2023. "Tourism combination forecasting using a dynamic weighting strategy with change-point analysis," Current Issues in Tourism, Taylor & Francis Journals, vol. 26(14), pages 2357-2374, July.
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