Group pooling for deep tourism demand forecasting

Yishuo Zhang, Gang Li*, Birgit Muskat, Rob Law, Yating Yang

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    56 Citations (Scopus)

    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.”

    Original languageEnglish
    Article number102899
    JournalAnnals of Tourism Research
    Volume82
    DOIs
    Publication statusPublished - May 2020

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