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Group pooling for deep tourism demand forecasting

Version 2 2024-06-06, 00:18
Version 1 2020-03-23, 13:10
journal contribution
posted on 2024-06-06, 00:18 authored by Yishuo ZhangYishuo Zhang, Gang LiGang Li, B Muskat, R Law, Y Yang
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.”

History

Journal

Annals of Tourism Research

Volume

82

Article number

ARTN 102899

Pagination

1 - 17

Location

Amsterdam, The Netherlands

ISSN

0160-7383

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2020, Elsevier Ltd

Publisher

PERGAMON-ELSEVIER SCIENCE LTD