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Neural network forecasting of tourism demand

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

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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
Uncontrolled Keywords: ResPubID8556. neural network, basic structural, tourism forecasting, Singapore forecasting
Subjects: Historical > RFCD Classification > 340000 Economics
Historical > Faculty/School/Research Centre/Department > School of Economics and Finance
Current > FOR Classification > 1401 Economic Theory
Depositing User: Ms Phung T Tran
Date Deposited: 10 Nov 2008 23:50
Last Modified: 19 Oct 2020 01:05
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Citations in Scopus: 84 - View on Scopus

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