IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-642-40060-5_97.html
   My bibliography  Save this book chapter

A Forecasting Model for Short Term Tourist Arrival Based on the Empirical Mode Decomposition and Support Vector Regression

In: Proceedings of 2013 4th International Asia Conference on Industrial Engineering and Management Innovation (IEMI2013)

Author

Listed:
  • Jun Wang

    (Sichuan University)

  • Ming-ming Hu

    (Sichuan University)

  • Peng Ge

    (Sichuan University)

  • Pei-yu Ren

    (Sichuan University)

  • Rong Zhao

    (Sichuan University)

Abstract

In this study, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Least Squares Support Vector Machines (LSSVMs) is proposed to predict tourism demand (i.e. the maximal number of arrivals in a short time interval). The proposed approach first uses EMD decompose the complicated data into a finite set of Intrinsic Mode Functions (IMFs) and a residue, then the IMF components and residue are modeled and forecasted using Least Squares Support Vector Machines, next, the forecasting values are obtained by the sum of these prediction results. In order to evaluate the performance of the proposed approach, the maximal values of tourist arrive in 1 min time interval is used as an illustrative example. Experimental results show that the proposed model outperforms the single LSSVM model without EMD preprocessing.

Suggested Citation

  • Jun Wang & Ming-ming Hu & Peng Ge & Pei-yu Ren & Rong Zhao, 2014. "A Forecasting Model for Short Term Tourist Arrival Based on the Empirical Mode Decomposition and Support Vector Regression," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), Proceedings of 2013 4th International Asia Conference on Industrial Engineering and Management Innovation (IEMI2013), edition 127, pages 1009-1021, Springer.
  • Handle: RePEc:spr:sprchp:978-3-642-40060-5_97
    DOI: 10.1007/978-3-642-40060-5_97
    as

    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.

    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:spr:sprchp:978-3-642-40060-5_97. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.