Using the combined model for forecasting the tourism demand

During the recent decade, the tourism industries have evolved evidently and quickly, pushing the bar higher for managers attempting to gain a competitive advantage in this industries. This study applies a combined model that combines the ACF, neural networks, and genetic algorithms for forecasting the tourism demand. Analytical results obtained using the ACF are entered as the input information of the neural network model. The proposed forecasting model outperforms other model in forecasting the Taiwan's tourism demand from 2001 to 2009.