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• W3010745523 endingPage "102899" @default.
• W3010745523 startingPage "102899" @default.
• W3010745523 abstract "Advances in tourism demand forecasting immensely benefit tourism and other sectors, such as economic and resource management studies. However, even for novel AI-based methodologies, the challenge of limited available data causing model overfitting and high complexity in forecasting models remains a major problem. This study proposes a novel group-pooling-based deep-learning model (GP–DLM) to address these problems and improve model accuracy. Specifically, with our group-pooling method, we advance the tourism forecasting literature with the following findings. First, GP–DLM provides superior accuracy in comparison with benchmark models. Second, we define the novel dynamic time warping (DTW) clustering quantitative approach. Third, we reveal cross-region factors that influence travel demands of the studied regions, including “travel blog,” “best food,” and “Air Asia.”" @default.
• W3010745523 created "2020-03-23" @default.
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• W3010745523 date "2020-05-01" @default.
• W3010745523 modified "2023-10-14" @default.
• W3010745523 title "Group pooling for deep tourism demand forecasting" @default.
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• W3010745523 doi "https://doi.org/10.1016/j.annals.2020.102899" @default.
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