ABSTRACT
The tourism industry possesses many data, which appear in a non-numeric form. Most of the existing studies on tourism demand forecasting apply economic models that use mathematical functions, which require many statistical assumptions and limitations. This chapter 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 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 of the test data. Japan was the major source market for Hong Kong's tourism industry during the study period.
