Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25133
Title: Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting
Authors: Law, R 
Keywords: Back-propagation
Feed-forward
Neural networks
Tourism forecasting
Issue Date: 2000
Publisher: Pergamon Press
Source: Tourism management, 2000, v. 21, no. 4, p. 331-340 How to cite?
Journal: Tourism management 
Abstract: 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 have achieved a certain degree of success in tourism forecasting, are unable to represent the relationship of demand for tourism as accurate as a multiprocessing node-based feed-forward neural network. Previous research has demonstrated 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 applicability of neural networks in tourism demand forecasting by incorporating the back-propagation learning process into a non-linearly separable tourism demand data. Empirical results indicate that utilizing a back-propagation neural network outperforms regression models, time-series models, and feed-forward neural networks in terms of forecasting accuracy.
URI: http://hdl.handle.net/10397/25133
ISSN: 0261-5177
EISSN: 1879-3193
DOI: 10.1016/S0261-5177(99)00067-9
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