Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting

Traditional tourism demand forecasting techniques concentrate predominantly on multivariate regression models and univariate time-series models. These single mathematical function-based forecasting techniques, although they h ave achieved a certain degree of success in tourism forecasting, are unable to represent the relationship of demand for tourism as accurate as a multi processing node-based feed-forward neural network. Previous research has de monstrated that using a feed-forward neural network can accomplish a higher forecasting accuracy than the regression and time-series techniques for a set of linearly separable tourism demand data. This research extends the ap plicability of neural networks in tourism demand forecasting by incorporati ng the backpropagation learning process into a non-linearly separable touri sm demand data. Empirical results indicate that utilizing a backpropagation neural network outperforms regression models, time-series models, and feed -forward neural networks in terms of forecasting accuracy. (C) 2000 Elsevie r Science Ltd. All rights reserved.