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Tourism Technical Analysis System

Author

Listed:
  • C. Petropoulos

    (Forecasting Systems Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, 15773 Zografou Athens, Greece)

  • K. Nikolopoulos

    (Supply Chain Management Research Group, Decision Sciences and Operations Management, Manchester Business School, University of Manchester, Manchester M15 6PB, UK)

  • A. Patelis

    (Forecasting Systems Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, 15773 Zografou Athens, Greece)

  • V. Assimakopoulos

    (Secretary for the Information Society, Ministry of Economy and Finance, 5-7 Nikis Str, 10180 Athens, Greece)

  • D. Askounis

    (Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, 15773 Zografou Athens, Greece)

Abstract

Accurate forecasts of tourism demand are prerequisites in the decision making process in many organizations in the private and public sectors. Any information concerning the future evolution of tourism flows is of great importance to hoteliers, tour operators and other industries concerned with tourism, services or transportation. In the last few decades international tourism demand has attracted substantial academic interest, resulting in a wide range of successful forecasting approaches. Much attention has been paid to econometric models that use regression techniques to estimate the underlying relationship between tourism demand and its determinants; unfortunately, empirical studies suggest that these models usually fail to outperform simple time series models. The current study focuses on an alternative approach, the Tourism Technical Analysis System (TTAS), incorporating the use of technical analysis techniques and building on the similarities between stock and tourism markets. The absolute and directional accuracy of TTAS is evaluated in relation to a range of time series and econometric methods for forecasting international tourism demand, using as a benchmark well-known published research.

Suggested Citation

  • C. Petropoulos & K. Nikolopoulos & A. Patelis & V. Assimakopoulos & D. Askounis, 2006. "Tourism Technical Analysis System," Tourism Economics, , vol. 12(4), pages 543-563, December.
    • Handle: RePEc:sae:toueco:v:12:y:2006:i:4:p:543-563
    DOI: 10.5367/000000006779320060
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    References listed on IDEAS

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    1. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
    2. Song, Haiyan & Witt, Stephen F. & Jensen, Thomas C., 2003. "Tourism forecasting: accuracy of alternative econometric models," International Journal of Forecasting, Elsevier, vol. 19(1), pages 123-141.
    3. 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.
    4. du Preez, Johann & Witt, Stephen F., 2003. "Univariate versus multivariate time series forecasting: an application to international tourism demand," International Journal of Forecasting, Elsevier, vol. 19(3), pages 435-451.
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    Cited by:

    1. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.

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