Analyzing and Forecasting Tourism Demand: A Rough Sets Approach

Abstract

This article reports a study that applies the rough sets algorithm to tourism demand analysis. Empirical outcomes are a set of automated but practical decision rules for practitioners from data that have a high degree of vagueness. We also introduce two new measures of qualitative noneconomic factors, namely a leisure time index and climate index into the forecasting framework. On the basis of long-haul U.S. and U.K. tourism demand for Hong Kong, empirical results show that leisure time and climate have stronger impacts on tourist arrivals than economic factors. Comprehensible decision rules are generated and tourism demand forecasts attain an accuracy of up to 80%. The findings put forward the importance of qualitative non- economic factors in travel motivation theory and demand analysis.

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