TY - JOUR
T1 - Group pooling for deep tourism demand forecasting
AU - Zhang, Yishuo
AU - Li, Gang
AU - Muskat, Birgit
AU - Law, Rob
AU - Yang, Yating
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/5
Y1 - 2020/5
N2 - 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.”
AB - 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.”
KW - AI-based methodology
KW - Asia Pacific travel patterns
KW - Deep-learning model
KW - Group-pooling method
KW - Tourism demand forecasting
KW - Tourism demand similarity
UR - http://www.scopus.com/inward/record.url?scp=85081297614&partnerID=8YFLogxK
U2 - 10.1016/j.annals.2020.102899
DO - 10.1016/j.annals.2020.102899
M3 - Article
SN - 0160-7383
VL - 82
JO - Annals of Tourism Research
JF - Annals of Tourism Research
M1 - 102899
ER -