IDEAS home Printed from
   My bibliography  Save this book chapter

Forecasting Tourism Demand in Europe

In: Operational Research in Agriculture and Tourism


  • Dimitrios I. Vortelinos

    (University of Lincoln)

  • Konstantinos Gkillas

    (University of Patras)

  • Christos Floros

    (Hellenic Mediterranean University)

  • Lavrentios Vasiliadis

    (University of Patras)


We study the performance of the k nearest neighbor (kNN) forecasts in the context of European tourism demand. The forecasting performance of neural networks is examined across different parameterizations of the kNN model. The selection of the most appropriate kNN parametrization can produce more accurate forecasts. Tourism demand is forecast monthly for 20 European countries. Tourism demand is measured via seven variables for the reason of consistency in results. kNNs better forecast tourism demand in shorter horizons; in specific, 1 month ahead. The parametrization of the kNN model affects forecasting performance. More sophisticated parameterizations perform better than either an ARIMA model or a naive kNN parametrization. The inclusion of international stock indices significantly increases forecasting accuracy. The more explanatory variables employed, the higher forecasting accuracy is retrieved. However, there is not a specific group of stock markets affecting more the kNN model’s forecasting accuracy. The forecasting accuracy of kNNs differs between three (Western, Eastern and Southern) European regions.

Suggested Citation

  • Dimitrios I. Vortelinos & Konstantinos Gkillas & Christos Floros & Lavrentios Vasiliadis, 2020. "Forecasting Tourism Demand in Europe," Cooperative Management, in: Evangelia Krassadaki & George Baourakis & Constantin Zopounidis & Nikolaos Matsatsinis (ed.), Operational Research in Agriculture and Tourism, pages 107-129, Springer.
  • Handle: RePEc:spr:comchp:978-3-030-38766-2_6
    DOI: 10.1007/978-3-030-38766-2_6

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.


    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:spr:comchp:978-3-030-38766-2_6. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (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 hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.