IDEAS home Printed from https://ideas.repec.org/a/sae/toueco/v11y2005i3p301-328.html
   My bibliography  Save this article

Neural Network Forecasting of Tourism Demand

Author

Listed:
  • Sen Cheong Kon
  • Lindsay W. Turner

Abstract

In times of tourism uncertainty, practitioners need short-term forecasting methods. This study compares the forecasting accuracy of the basic structural method (BSM) and the neural network method to find the best structure for neural network models. Data for arrivals to Singapore are used to test the analysis while the naïve and Holt-Winters methods are used for base comparison of simpler models. The results confirm that the BSM remains a highly accurate method and that correctly structured neural models can outperform BSM and the simpler methods in the short term, and can also use short data series. These findings make neural methods significant candidates for future research.

Suggested Citation

  • Sen Cheong Kon & Lindsay W. Turner, 2005. "Neural Network Forecasting of Tourism Demand," Tourism Economics, , vol. 11(3), pages 301-328, September.
    • Handle: RePEc:sae:toueco:v:11:y:2005:i:3:p:301-328
    DOI: 10.5367/000000005774353006
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.5367/000000005774353006
    Download Restriction: no
    File URL: https://libkey.io/10.5367/000000005774353006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Arnold Zellner, 1978. "Seasonal Analysis of Economic Time Series," NBER Books, National Bureau of Economic Research, Inc, number zell78-1.
    2. Qi, Min & Zhang, Guoqiang Peter, 2001. "An investigation of model selection criteria for neural network time series forecasting," European Journal of Operational Research, Elsevier, vol. 132(3), pages 666-680, August.
    3. Chiang, W. -C. & Urban, T. L. & Baldridge, G. W., 1996. "A neural network approach to mutual fund net asset value forecasting," Omega, Elsevier, vol. 24(2), pages 205-215, April.
    4. Lindsay W. Turner & Stephen F. Witt, 2001. "Forecasting Tourism Using Univariate and Multivariate Structural Time Series Models," Tourism Economics, , vol. 7(2), pages 135-147, June.
    5. Harvey, A C & Todd, P H J, 1983. "Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 299-307, October.
    6. Lindsay W. Turner & Yvette Reisinger & Stephen F. Witt, 1998. "Tourism Demand Analysis Using Structural Equation Modelling," Tourism Economics, , vol. 4(4), pages 301-323, December.
    7. Gorr, Wilpen L. & Nagin, Daniel & Szczypula, Janusz, 1994. "Comparative study of artificial neural network and statistical models for predicting student grade point averages," International Journal of Forecasting, Elsevier, vol. 10(1), pages 17-34, June.
    8. Nerlove, Marc & Grether, David M. & Carvalho, José L., 1979. "Analysis of Economic Time Series," Elsevier Monographs, Elsevier, edition 1, number 9780125157506 edited by Shell, Karl.
    9. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    10. Harvey, A C & Todd, P H J, 1983. "Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study: Response," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 313-315, October.
    11. Genshiro Kitagawa, 1981. "A Nonstationary Time Series Model And Its Fitting By A Recursive Filter," Journal of Time Series Analysis, Wiley Blackwell, vol. 2(2), pages 103-116, March.
    12. Christine Lim, 1997. "An Econometric Classification and Review of International Tourism Demand Models," Tourism Economics, , vol. 3(1), pages 69-81, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Victor Gomez & Jorg Breitung, 1999. "The Beveridge–Nelson Decomposition: A Different Perspective with New Results," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(5), pages 527-535, September.
    2. Ester Ruiz & Fernando Lorenzo, 1997. "Prediction with univariate time series models: The Iberia case," Documentos de Trabajo (working papers) 0298, Department of Economics - dECON.
    3. Fackler, Paul L., 1989. "Modeling Trend and Higher Moment Properties of U.S. Corn Yields," 1989 Quantifying Long Run Agricultural Risks and Evaluating Farmer Responses to Risk Meeting, April 9-12, 1989, Sanibel Island, Florida 271523, Regional Research Projects > S-232: Quantifying Long Run Agricultural Risks and Evaluating Farmer Responses to Risk.
    4. Maravall, Agustin & Planas, Christophe, 1999. "Estimation error and the specification of unobserved component models," Journal of Econometrics, Elsevier, vol. 92(2), pages 325-353, October.
    5. Breitung, Jörg, 1998. "On model based seasonal adjustment procedures," SFB 373 Discussion Papers 1998,12, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    6. Rasi, Chris-Marie & Viikari, Jan-Markus, 1998. "The time-varying NAIRU and potential output in Finland," Research Discussion Papers 6/1998, Bank of Finland.
    7. Jo C. Vu, 2006. "Effect of Demand Volume on Forecasting Accuracy," Tourism Economics, , vol. 12(2), pages 263-276, June.
    8. Thury, Gerhard & Witt, Stephen F., 1998. "Forecasting industrial production using structural time series models," Omega, Elsevier, vol. 26(6), pages 751-767, December.
    9. Maravall, Agustín, 1999. "Short-term and long-term trends, seasonal and the business cycle," DES - Working Papers. Statistics and Econometrics. WS 6291, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Chen, Chunhang, 1997. "Robustness properties of some forecasting methods for seasonal time series: A Monte Carlo study," International Journal of Forecasting, Elsevier, vol. 13(2), pages 269-280, June.
    11. Pablo Marshall, 1998. "Prediccion De Series De Ventas: Un Analisis De Cointegracion Con El Pib," Abante, Escuela de Administracion. Pontificia Universidad Católica de Chile., vol. 1(1), pages 89-109.
    12. repec:zbw:bofrdp:1998_006 is not listed on IDEAS
    13. Shujie Shen & Gang Li & Haiyan Song, 2009. "Effect of Seasonality Treatment on the Forecasting Performance of Tourism Demand Models," Tourism Economics, , vol. 15(4), pages 693-708, December.
    14. Ardeni, Pier Giorgio & Wright, Brian, 1990. "The long term behavior of commodity prices," Policy Research Working Paper Series 358, The World Bank.
    15. Franses, P.H. & McAleer, M., 1995. "Testing Nested and Non-Nested Periodically Integrated Autoregressive Models," Papers 9510, Tilburg - Center for Economic Research.
    16. Marczak, Martyna & Proietti, Tommaso, 2016. "Outlier detection in structural time series models: The indicator saturation approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 180-202.
    17. Gabriele Fiorentini & Enrique Sentana, 2016. "Neglected serial correlation tests in UCARIMA models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 121-178, March.
    18. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
    19. Marczak, Martyna & Proietti, Tommaso & Grassi, Stefano, 2018. "A data-cleaning augmented Kalman filter for robust estimation of state space models," Econometrics and Statistics, Elsevier, vol. 5(C), pages 107-123.
    20. Yasemin Deniz Akarım, 2013. "A Comparison of Linear and Nonlinear Models in Forecasting Market Risk: The Evidence from Turkish Derivative Exchange," Journal of Economics and Behavioral Studies, AMH International, vol. 5(3), pages 164-172.
    21. Peter Young, 1999. "Recursive and en-bloc approaches to signal extraction," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(1), pages 103-128.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:toueco:v:11:y:2005:i:3:p:301-328. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.