Tourist arrival forecasting by evolutionary fuzzy systems

Accurate forecasts of tourist arrivals and study of the tourist arrival patterns are essential for the tourism-related industries to formulate efficient and effective strategies on maintaining and boosting tourism industry in a country. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. This study presents a hybrid artificial intelligence (AI) model to develop a Mamdani-type fuzzy rule-based system to forecast tourist arrivals with high accuracy. The hybrid model uses genetic algorithm for learning rule base and tuning data base of fuzzy system. Actually it extracts useful information patterns with a descriptive rule induction approach based on Genetic Fuzzy Systems (GFS). This is the first study on using a GFS with the ability of learning rule base and tuning data base of fuzzy system for tourist arrival forecasting problem. Evaluation of the proposed approach will be carried out by applying it to a case study of tourist arrivals to Taiwan and results will be compared with other studies which have used the same data set. Results show that the proposed approach has high accuracy, so it can be considered as a suitable tool for tourism arrival forecasting problems.