Difference between revisions of "Research Project: A Decision Rules Based Forecasting Model for Tourism Demand in Hong Kong"

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<div align="right">''[http://wiki.ifitt.org/index.php/E-tourism_Knowledge_Map <- back to IFITT eTourism Knowledge Map]''</div>
<div align="right">''[http://wiki.ifitt.org/index.php/E-Tourism_Institutions <- e-Tourism Institutions]''</div>  
<div align="right">''[http://wiki.ifitt.org/index.php/E-tourism_research <-- back to overview of e-Tourism research areas]''</div> [[File:IFITT_eTourismKnowledgeMap_LOW-RES_20.jpg|link=http://wiki.ifitt.org/index.php/E-tourism_Knowledge_Map]]
<div align="right">''[http://wiki.ifitt.org/index.php/E-tourism_research <- e-Tourism Research Areas]''</div>
<div align="right">''[http://wiki.ifitt.org/index.php/E-tourism_education <- e-Tourism Education Programs]''</div>[[File:IFITT_eTourismKnowledgeMap_LOW-RES_20.jpg|link=http://wiki.ifitt.org/index.php/E-tourism_Knowledge_Map]]

Latest revision as of 09:56, 25 September 2015

<- e-Tourism Institutions
<- e-Tourism Research Areas
<- e-Tourism Education Programs
IFITT eTourismKnowledgeMap LOW-RES 20.jpg


Most of the existing studies on tourism demand forecasting apply economic models that use mathematical functions, which require many statistical assumptions and limitations. This project is 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.


Tourism demand forecasting, rough sets, Information Table, condition attributes, decision attribute

Research Institute

HongKong Polytechnic University

Funding Institution

Official project number

Status and date of completion

Completed, 2004