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A technical analysis approach to tourism demand forecasting

Author

Listed:
  • C. Petropoulos
  • K. Nikolopoulos
  • A. Patelis
  • V. Assimakopoulos

Abstract

Tourism demand forecasts are of great economic value both for the public and private sector. Any information concerning the future evolution of tourism flows, is of great importance to hoteliers, tour operators and other industries concerned with tourism or transportation, in order to adjust their policy and corporate finance. In the last few decades, numerous researchers have studied international tourism demand and a wide range of the available forecasting techniques have been tested. Major focus has been given to econometric studies that involve the use of least squares regression to estimate the quantitative relationship between tourism demand and its determinants. However, econometric models usually fail to outperform simple time series extrapolative models. This article introduces a new approach to tourism demand forecasting via incorporating technical analysis techniques. The proposed model is evaluated versus a range of classic univariate time series methods in terms of forecasting and directional accuracy.

Suggested Citation

  • C. Petropoulos & K. Nikolopoulos & A. Patelis & V. Assimakopoulos, 2005. "A technical analysis approach to tourism demand forecasting," Applied Economics Letters, Taylor & Francis Journals, vol. 12(6), pages 327-333.
    • Handle: RePEc:taf:apeclt:v:12:y:2005:i:6:p:327-333
    DOI: 10.1080/13504850500065745
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    References listed on IDEAS

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    1. Christine Lim & Michael McAleer, 2001. "Time Series Forecasts of International Tourism Demand for Australia," ISER Discussion Paper 0533, Institute of Social and Economic Research, Osaka University.
    2. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    3. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
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    Cited by:

    1. Day, Min-Yuh & Ni, Yensen & Huang, Paoyu, 2019. "Trading as sharp movements in oil prices and technical trading signals emitted with big data concerns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 349-372.
    2. C. Petropoulos & K. Nikolopoulos & A. Patelis & V. Assimakopoulos & D. Askounis, 2006. "Tourism Technical Analysis System," Tourism Economics, , vol. 12(4), pages 543-563, December.
    3. Lin, Tun & De Guzman, Franklin, 2007. "Tourism for pro-poor and sustainable growth: economic analysis of tourism projects," MPRA Paper 24994, University Library of Munich, Germany.
    4. Dr. Murat çuhadar & Iclal Cogurcu & Ceyda Kukrer, 2014. "Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(3), pages 12-28, March.
    5. Eleonora Di Matteo & Paolo Roma & Santo Zafonte & Umberto Panniello & Lorenzo Abbate, 2021. "Development of a Decision Support System Framework for Cultural Heritage Management," Sustainability, MDPI, vol. 13(13), pages 1-27, June.
    6. Dr. Murat çuhadar & Iclal Cogurcu & Ceyda Kukrer, 2014. "Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures," International Journal of Business and Social Research, LAR Center Press, vol. 4(3), pages 12-28, March.
    7. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Combination of long term and short term forecasts, with application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 870-886, July.
    8. Marcos Álvarez-Díaz & Manuel González-Gómez & María Soledad Otero-Giráldez, 2018. "Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming," Forecasting, MDPI, vol. 1(1), pages 1-17, September.
    9. Edy Fradinata & Sakesun Suthummanon & Wannarat Suntiamorntut, 2015. "Forecasting Determinant of Cement Demand in Indonesia with Artificial Neural Network," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 5(7), pages 373-384, July.
    10. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
    11. Vicky Bamiatzi & Konstantinos Bozos & Konstantinos Nikolopoulos, 2010. "On the predictability of firm performance via simple time-series and econometric models: evidence from UK SMEs," Applied Economics Letters, Taylor & Francis Journals, vol. 17(3), pages 279-282, February.

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