Forecasting daily tourism volume: a hybrid approach with CEMMDAN and multi-kernel adaptive ensemble-Reference-Cited by-同舟云学术

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Forecasting daily tourism volume: a hybrid approach with CEMMDAN and multi-kernel adaptive ensemble

Published:2022-03-23 Issue:7 Volume:26 Page:1112-1131
ISSN:1368-3500
Container-title:Current Issues in Tourism
language:en
Short-container-title:Current Issues in Tourism

Author:

Zhao Erlong¹,Du Pei¹,Azaglo Ernest Young¹,Wang Shouyang²³⁴^ORCID,Sun Shaolong¹

Affiliation:

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

2. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, People’s Republic of China

3. School of Economics and Management, University of Chinese Academy of Sciences, Beijing, People’s Republic of China

4. Center for Forecasting Science, Chinese Academy of Sciences, Beijing, People’s Republic of China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

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

Link

https://www.tandfonline.com/doi/pdf/10.1080/13683500.2022.2048806

Reference50 articles.

1. A novel approach to model selection in tourism demand modeling

2. Signal Extraction and Forecasting of the UK Tourism Income Time Series: A Singular Spectrum Analysis Approach

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

4. Daily tourism volume forecasting for tourist attractions

5. Forecasting tourism demand based on empirical mode decomposition and neural network

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