Neural network forecasting of tourism demand

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http://eprints.org/relation/isVersionOf https://vuir.vu.edu.au/id/dark_document/50634 http://eprints.org/relation/isVolatileVersionOf https://vuir.vu.edu.au/id/dark_document/50634 http://eprints.org/relation/issmallThumbnailVersionOf https://vuir.vu.edu.au/id/dark_document/50634 50640 2 2375972 document 50640 lightbox.jpg image/png ccd3f90eb9b28489bfee5fe4de8f4ae4 MD5 37683 2020-06-10 00:55:32 https://vuir.vu.edu.au/1601/7/lightbox.jpg 1601 7 7 image/png other Thumbnails conversion from text to thumbnail_lightbox en staffonly

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http://eprints.org/relation/isVersionOf https://vuir.vu.edu.au/id/dark_document/50635 http://eprints.org/relation/isVolatileVersionOf https://vuir.vu.edu.au/id/dark_document/50635 http://eprints.org/relation/issmallThumbnailVersionOf https://vuir.vu.edu.au/id/dark_document/50635 archive 293 disk0/00/00/16/01 2008-11-10 23:50:21 2021-08-27 07:41:47 2008-11-10 23:50:21 article show Kon Sen Cheong Kon, Sen Cheong Turner Lindsay W Turner, Lindsay W Neural network forecasting of tourism demand pub 340000 AppliedEconomics FOR_2008_140100 ResPubID8556. neural network, basic structural, tourism forecasting, Singapore forecasting 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. 2005 published IP Publishing http://dx.doi.org/10.5367/000000005774353006 50635 2 2375962 dark_document 50635 IPPTurner091006.pdf application/pdf bad16fcf79826b3b6d4d2df1bd54289d MD5 31547 2020-06-10 00:45:27 1601 2 2 application/pdf text Publisher Permission en staffonly

IPPTurner091006.pdf

other none Tourism Economics 11 3 301-328 TRUE 1354-8166 Includes bibliographical references: pp. 323-328 FALSE 116 2-s2.0-26044463276 2023-07-02 04:00:32