Neural network forecasting of tourism demand

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Kon, Sen Cheong and Turner, Lindsay W (2005) Neural network forecasting of tourism demand. Tourism Economics, 11 (3). pp. 301-328. ISSN 1354-8166

Abstract

In times of tourism uncertainty, practitioners need short-term forecasting methods. This study compares the forecasting accuracy of the basic structural method (BSM) and the neural network method to find the best structure for neural network models. Data for arrivals to Singapore are used to test the analysis while the naïve and Holt-Winters methods are used for base comparison of simpler models. The results confirm that the BSM remains a highly accurate method and that correctly structured neural models can outperform BSM and the simpler methods in the short term, and can also use short data series. These findings make neural methods significant candidates for future research.

Item type Article
URI https://vuir.vu.edu.au/id/eprint/1601
Official URL http://dx.doi.org/10.5367/000000005774353006
Subjects Historical > RFCD Classification > 340000 Economics
Historical > Faculty/School/Research Centre/Department > School of Economics and Finance
Historical > FOR Classification > 1401 Economic Theory
Keywords ResPubID8556. neural network, basic structural, tourism forecasting, Singapore forecasting
Citations in Scopus 93 - View on Scopus
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