IDEAS home Printed from https://ideas.repec.org/a/taf/rcitxx/v26y2023i10p1573-1592.html
   My bibliography  Save this article

Daily tourism demand forecasting: the impact of complex seasonal patterns and holiday effects

Author

Listed:
  • Yunhao Liu
  • Gengzhong Feng
  • Kwai-Sang Chin
  • Shaolong Sun
  • Shouyang Wang

Abstract

Daily tourism demand forecasting can provide important implications for the tourism industry. However, there exist limitations in applying traditional methods to forecast daily tourism demand because of the complex seasonal patterns and holiday effects. In this study, we introduce FB Prophet and apply it to the forecasting of daily tourism demand in the Jiuzhai Valley National Park and Macao from Mainland China. The decomposition result of FB Prophet shows its ability to handle the influence of seasonal patterns and holiday effects. The forecasting results show that considering seasonal patterns, holiday effects, and other predictors can significantly improve the forecasting performance, and FB Prophet outperforms other methods.

Suggested Citation

  • Yunhao Liu & Gengzhong Feng & Kwai-Sang Chin & Shaolong Sun & Shouyang Wang, 2023. "Daily tourism demand forecasting: the impact of complex seasonal patterns and holiday effects," Current Issues in Tourism, Taylor & Francis Journals, vol. 26(10), pages 1573-1592, May.
    • Handle: RePEc:taf:rcitxx:v:26:y:2023:i:10:p:1573-1592
    DOI: 10.1080/13683500.2022.2060067
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13683500.2022.2060067
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13683500.2022.2060067?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ke Xu & Junli Zhang & Junhao Huang & Hongbo Tan & Xiuli Jing & Tianxiang Zheng, 2024. "Forecasting Visitor Arrivals at Tourist Attractions: A Time Series Framework with the N-BEATS for Sustainable Tourism," Sustainability, MDPI, vol. 16(18), pages 1-31, September.

    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:26:y:2023:i:10:p:1573-1592. 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.