IDEAS home Printed from https://ideas.repec.org/a/taf/rcitxx/v27y2024i8p1208-1228.html
   My bibliography  Save this article

Daily tourism demand forecasting before and during COVID-19: data predictivity and an improved decomposition-ensemble framework

Author

Listed:
  • Yanzhao Li
  • Dongchuan Yang
  • Ju’e Guo
  • Shaolong Sun
  • Shouyang Wang

Abstract

The global tourism industry is struggling to recover from the COVID-19 pandemic. During the COVID-19 pandemic, daily tourism forecasting is more critical than ever before in supporting decisions and planning. Considering the changes in tourist psyche and behaviour caused by COVID-19, this study attempts to investigate whether the statistical modelling methods can work reliably under the new normal when travel restrictions are eased or lifted. To this end, we first compare the predictivity of daily tourism demand data before and during COVID-19, and observe heterogeneous impacts across different geographical scales. Then an improved multivariate & multiscale decomposition-ensemble framework is proposed to forecast daily tourism demand. The empirical study indicates the superiority and practicability of the proposed framework before and during COVID-19. Finally, we call for more research on the comparability of tourism demand forecasting.

Suggested Citation

  • Yanzhao Li & Dongchuan Yang & Ju’e Guo & Shaolong Sun & Shouyang Wang, 2024. "Daily tourism demand forecasting before and during COVID-19: data predictivity and an improved decomposition-ensemble framework," Current Issues in Tourism, Taylor & Francis Journals, vol. 27(8), pages 1208-1228, April.
    • Handle: RePEc:taf:rcitxx:v:27:y:2024:i:8:p:1208-1228
    DOI: 10.1080/13683500.2023.2202308
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13683500.2023.2202308
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13683500.2023.2202308?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    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:taf:rcitxx:v:27:y:2024:i:8:p:1208-1228. 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.

    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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/rcit .

    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.