Regional tourism demand forecasting with spatiotemporal interactions: a multivariate decomposition deep learning model-Reference-Cited by-同舟云学术

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Regional tourism demand forecasting with spatiotemporal interactions: a multivariate decomposition deep learning model

Published:2023-06-03 Issue:6 Volume:28 Page:625-646
ISSN:1094-1665
Container-title:Asia Pacific Journal of Tourism Research
language:en
Short-container-title:Asia Pacific Journal of Tourism Research

Author:

Yang Dongchuan¹²^ORCID,Li Yanzhao¹³^ORCID,Guo Ju’e¹^ORCID,Li Gang⁴^ORCID,Sun Shaolong¹^ORCID

Affiliation:

1. School of Management, Xi'an Jiaotong University, Xi’an, People’s Republic of China

2. NUS Business School, National University of Singapore, Singapore, Singapore

3. Nanyang Business School, Nanyang Technological University, Singapore, Singapore

4. Center for Cyber Resilience and Trust, Deakin University, Burwood, Australia

Funder

China Scholarship Council

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Basic Research Program of Shaanxi Province

Science and Technology Project of China Huaneng

Publisher

Informa UK Limited

Subject

Tourism, Leisure and Hospitality Management,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10941665.2023.2256431

Reference47 articles.

1. Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model

2. Bagging in Tourism Demand Modeling and Forecasting

3. Forecasting Daily Tourism Demand for Tourist Attractions with Big Data: An Ensemble Deep Learning Method

4. Tourism demand forecasting with time series imaging: A deep learning model

5. Interdependence of International Tourism Demand and Volatility in Leading ASEAN Destinations

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1. Machine learning applied to tourism: A systematic review;WIREs Data Mining and Knowledge Discovery;2024-07-04

2. Tourism demand modelling and forecasting: a Horizon 2050 paper;Tourism Review;2024-04-24

3. Tourism demand forecasting using complex network theory;Asia Pacific Journal of Tourism Research;2024-03-03

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