Forecasting tourism demand of tourist attractions during the COVID-19 pandemic-Reference-Cited by-同舟云学术

Forecasting tourism demand of tourist attractions during the COVID-19 pandemic

Published:2023-02-07 Issue:3 Volume:27 Page:445-463
ISSN:1368-3500
Container-title:Current Issues in Tourism
language:en
Short-container-title:Current Issues in Tourism

Author:

Chen Dilin¹,Sun Fenglan²,Liao Zhixue²

Affiliation:

1. School of International Education, Southwest Jiaotong University, Chengdu, People’s Republic of China

2. School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, People’s Republic of China

Funder

National Natural Science Foundation of China

Humanities and Social Science Projects of the Ministry of Education of China

Publisher

Informa UK Limited

Subject

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

Link

https://www.tandfonline.com/doi/pdf/10.1080/13683500.2023.2165482

Reference49 articles.

1. A Survey of Techniques for Event Detection in Twitter

2. Crogging (cross-validation aggregation) for forecasting — A novel algorithm of neural network ensembles on time series subsamples

3. Cross-validation aggregation for combining autoregressive neural network forecasts

4. A note on the validity of cross-validation for evaluating autoregressive time series prediction

5. Evaluating time series forecasting models: an empirical study on performance estimation methods

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Forecasting international tourist arrivals in South Korea: a deep learning approach;Journal of Hospitality and Tourism Technology;2024-08-20

2. Short- and long-term destination resilience in Wuhan, China under COVID-19: Differences in scenic spot type and origin market structure;International Journal of Disaster Risk Reduction;2024-08

3. Tourism and uncertainty: a machine learning approach;Current Issues in Tourism;2024-07-02

4. Tourism forecasts after COVID-19: Evidence of Portugal;Annals of Tourism Research Empirical Insights;2024-05

5. Short- and Long-Term Tourism Resilience in Wuhan, China Under Covid-19: Differences in Scenic Spot Type and Visitor Origin;2023