Tourist Arrival Forecasting Using Adaptive Fuzzy Network
Sheau-Chi Jiang · 2007
International tourism has become one of the largest and most rapidly growing industries in the world. Since there exists the perishable nature of the product and service in the tourism industry, it is crucial to have an accurate forecast of its international visitors and tourism receipts in order to choose an appropriate strategy for its economic benefits. In this paper, a new approach is proposed and that is a fully connected adaptive fuzzy network (AFN) based on Widrow-Hoff learning algorithm to model and forecast the tourist arrivals for the travel of international visitors to Taiwan. And the difference between the expected and the forecast output values falls into a very acceptable range of discrepancies, which means that using the adaptive fuzzy network has reached the required level of accuracy. The result is in good accord with the monitored data and allows its use as the forecasting model to help policy makers and managers of tourism industry to develop planning for various tourism activities.