Forecasting daily tourism demand with multiple factors

Shilin Xu, Yang Liu and Chun Jin

Annals of Tourism Research, 2023, vol. 103, issue C

Abstract: Various factors have contributed to forecasting tourism demand. Although deep learning methods can achieve accurate results, they haven't considered the temporal heterogeneity of multiple factors and lack interpretability. This study proposes a novel deep learning method for daily tourism demand forecasting. Benefiting from the encoder-decoder architecture, our method adequately exploits the temporal heterogeneity of multiple factors. Based on the attentional mechanism, our method provides an interpretation of tourism demand from both factors and temporal persistence patterns. The effectiveness of our method is verified through an empirical study of two tourist attractions before and during COVID-19. Our method compensates for the uninterpretability of deep learning models, which allows tourism managers to obtain deeper insights.

Keywords: Tourism demand forecasting; Temporal heterogenous multiple factors; Temporal fusion encoder-decoder with Bayesian optimization; Result interpretation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:103:y:2023:i:c:s0160738323001482

DOI: 10.1016/j.annals.2023.103675

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