Tourism Demand Forecasting: A Decomposed Deep Learning Approach
Date
2020
Authors
Zhang, Yishuo
Li, Gang
Muskat, Birgit
Law, Rob
Journal Title
Journal ISSN
Volume Title
Publisher
SAGE
Abstract
Tourism 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 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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Keywords
strategic tourism management, tourism demand forecasting, tourism planning, AI-based forecasting, deep learning, decomposing method, overfitting
Citation
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Source
Journal of Travel Research
Type
Journal article
Book Title
Entity type
Access Statement
Open Access
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Restricted until
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