Tourism demand forecasting: A deep learning approach

Rob Law, Gang Li, Davis Ka Chio Fong and Xin Han

Annals of Tourism Research, 2019, vol. 75, issue C, 410-423

Abstract: Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. Using a deep learning approach, this research studied the framework in forecasting monthly Macau tourist arrival volumes. The empirical results demonstrated that the deep learning approach significantly outperforms support vector regression and artificial neural network models. Moreover, the construction and identification of highly relevant features from the proposed deep network architecture provide practitioners with a means of understanding the relationships between various tourist demand forecasting factors and tourist arrival volumes.

Keywords: Tourism demand forecasting; Deep learning; Long-short-term-memory; Attention mechanism; Feature engineering; Lag order (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (71)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:75:y:2019:i:c:p:410-423

DOI: 10.1016/j.annals.2019.01.014

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