Neural Network Forecasting of Tourism Demand

Sen Cheong Kon and Lindsay W. Turner

Tourism Economics, 2005, vol. 11, issue 3, 301-328

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.

Keywords: neural network; basic structural; tourism forecasting; Singapore forecasting (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (41)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:11:y:2005:i:3:p:301-328

DOI: 10.5367/000000005774353006

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