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- W2082506859 abstract "In times of tourism uncertainty, practitioners need short-term forecastingmethods. This study compares the forecasting accuracy of thebasic structural method (BSM) and the neural network method tofind the best structure for neural network models. Data for arrivalsto Singapore are used to test the analysis while the naive and Holt-Winters methods are used for base comparison of simpler models.The results confirm that the BSM remains a highly accurate methodand that correctly structured neural models can outperform BSM andthe simpler methods in the short term, and can also use short dataseries. These findings make neural methods significant candidates forfuture research." @default.
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- W2082506859 date "2005-01-01" @default.
- W2082506859 modified "2023-09-28" @default.
- W2082506859 title "Neural network forescanting of tourism demand" @default.
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