engleski

Artificial neural networks implementation potentials - a literature review

The significant worldwide growth of the tourism sector in the two past decades resulted in an increased number of researches and studies of tourism industry determinants. In analysing and modelling core tourism components, mostly using secondary data, different methods and technique are applied ; different forecast models are constructed and compared using different forecast error measures. The aim of this research is to provide a detailed outline of different quantitative methods and techniques, both the traditional ones as well as some emerging most sophisticated methods used in modelling and forecasting tourism. Beside the various methods, there is a large variety of explanatory variables selection in constructing forecast models and explaining some core tourism industry components. This paper reviews the published researches and studies that use the Artificial Neural Networks in modelling and forecasting tourism industry. The most significant finding that resulted from the detailed desk research is that, beside their great implementation potentials, Artificial Neural Networks are underused in tourism analysing, although their forecast efficiency excel the usual traditional models.

artificial neural networks ; modelling and forecasting ; tourism

This paper has been financially supported by the University of Rijeka, for the project ZP UNIRI 4/17.