Tourism forecasting: A dynamic spatiotemporal model

Mengqiang Pan, Zhixue Liao, Zhouyiying Wang, Chi Ren, Zhibin Xing and Wenyong Li

Annals of Tourism Research, 2025, vol. 110, issue C

Abstract: In recent years, spatiotemporal modeling has become an effective method for predicting tourism demand. Nonetheless, existing forecasting models have neglected dynamic nature of spatial dependence. Furthermore, frequently used long short-term memory models often ignore spatial heterogeneity and are prone to overfitting in tourism contexts. To address these shortcomings, dynamic spatial-temporal convolutional network is proposed in this study. In this model, the spatial-temporal attention mechanism and convolution modules are employed to extract dynamic spatiotemporal dependencies and spatial heterogeneity. Based on two datasets with different time granularities, this empirical study shows that the proposed model outperforms baseline models. The results confirm that incorporating dynamic spatial dependencies and spatial heterogeneity can significantly improve predictive performance.

Keywords: Dynamic spatial dependencies; Spatial heterogeneity; Tourism forecasting; Graph neural network; Attention mechanism (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0160738324001488
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:110:y:2025:i:c:s0160738324001488

DOI: 10.1016/j.annals.2024.103871

Access Statistics for this article

Annals of Tourism Research is currently edited by John Tribe

More articles in Annals of Tourism Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().