Group pooling for deep tourism demand forecasting. (May 2020) - Zhang, Yishuo; Li, Gang; Muskat, Birgit; Law, Rob; Yang, Yating
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." Highlights: Our AI-based deep-learning approach contributes to higher forecasting accuracy. Our innovative deep-learning model alleviates the limited data availability. With our new pooling method, we significantly reduce model overfitting. We reveal similar cross-country demand patterns for the Asia-Pacific regions.