Modeling tourism demand: A decision rules based approach

Chun Hung Roberts Law, Carey Goh, Ray Pine

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

7 Citations (Scopus)

Abstract

Most of the existing studies on tourism demand forecasting apply economic models that usemathematical functions, which require many statistical assumptions and limitations. This paper presents a new approach that applies the rough sets theory to form a forecasting model for tourism demand. The objective of this research is to create patterns which are able to distinguish between the classes of arrivals in terms of volume, based upon differences in the characteristics in each arrival. The information about the arrivals was organized in an Information Table where the number of arrivals corresponds to condition attributes, and the classification was defined by a decision attribute that indicated the forecast categorical value of future arrivals. Utilizing Japanese arrivals data in Hong Kong, empirical results showed the induced decision rules could accurately forecast (86.5%) of the test data.
Original languageEnglish
Title of host publicationManagement Science Applications in Tourism and Hospitality
PublisherTaylor and Francis
Pages61-69
Number of pages9
ISBN (Print)9781315782478
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Condition attributes
  • Decision attribute
  • Information table
  • Rough sets
  • Tourism demand forecasting

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)

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