Neural network forecasting of tourism demand

Authors: Kon, Sen Cheong; Turner, Lindsay W.

Source: Tourism Economics, Volume 11, Number 3, September 2005 , pp. 301-328(28)

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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

Document Type: Research article

DOI: http://dx.doi.org/10.5367/000000005774353006

Publication date: 2005-09-01

More about this publication?

Tourism Economics, published bimonthly, is a peer-reviewed journal devoted to the economics and finance of tourism worldwide. Articles address the components of the tourism product (accommodation; restaurants; merchandizing; attractions; transport; entertainment; tourist activities); and the economic organization of tourism at micro and macro levels (market structure; role of public/private sectors; community interests; strategic planning; marketing; finance; economic development).
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