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 | 116 - View on Scopus |
| Download/View statistics | View download statistics for this item |
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