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Tourism Demand Forecasting: A Decomposed Deep Learning Approach
journal contribution
posted on 2021-05-01, 00:00 authored by Yishuo ZhangYishuo Zhang, Gang LiGang Li, Birgit Muskat, R LawTourism planners rely on accurate demand forecasting. However, despite numerous advancements, crucial methodological issues remain unaddressed. This study aims to further improve the modeling accuracy and advance the artificial intelligence (AI)-based tourism demand forecasting methods. Deep learning models that predict tourism demand are often highly complex and encounter overfitting, which is mainly caused by two underlying problems: (1) access to limited data volumes and (2) additional explanatory variable requirement. To address these issues, we use a decomposition method that achieves high accuracy in short- and long-term AI-based forecasting models. The proposed method effectively decomposes the data and increases accuracy without additional data requirement. In conclusion, this study alleviates the overfitting issue and provides a methodological contribution by proposing a highly accurate deep learning method for AI-based tourism demand modeling.
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Journal
Journal of Travel ResearchVolume
60Issue
5Article number
ARTN 0047287520919522Pagination
981 - 997Publisher
SAGE PUBLICATIONS INCPublisher DOI
ISSN
0047-2875eISSN
1552-6763Language
EnglishPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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