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

文献类型：期刊论文

作者Zhao, Erlong4; Du, Pei4; Azaglo, Ernest Young4; Wang, Shouyang1,2,3; Sun, Shaolong4
刊名CURRENT ISSUES IN TOURISM
出版日期2022-03-24
页码20
关键词Daily tourism volume forecasting decomposition ensemble approach sample entropy kernel extreme learning machine multi-kernel adaptive strategy
ISSN号1368-3500
DOI10.1080/13683500.2022.2048806
英文摘要Effective and timely forecasting of daily tourism volume is an important topic for tourism practitioners and researchers, which can reduce waste and promote the sustainable development of tourism. Several studies are based on the decomposition-ensemble model to forecast the time series of high volatility in tourism volume, but ignore different forecasting methods suitable for different subseries. This study provides an adaptive decomposition-ensemble hybrid forecasting approach. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to effectively decompose the original time series into multiple relatively easy subseries, which reduces the complexity of the data. Secondly, sample entropy calculates the complexity of a sequence, and then adopts the elbow rule to adaptively divide them into different complex sets. Finally, multi-kernel extreme learning machine (KELM) models are used to forecast the components of different sets and integrate them. This hybrid approach makes full use of the advantages of different models, which enables effective use of data. The empirical results demonstrate that the approach can both produce results that are close to the actual values and be utilized as a strategy for forecasting daily tourism volume.
资助项目National Natural Science Foundation of China[72101197] ; National Natural Science Foundation of China[71988101] ; Fundamental Research Funds for the Central Universities[SK2021007]
WOS研究方向Social Sciences - Other Topics
语种英语
WOS记录号WOS:000772339300001
出版者ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/60175]  
专题系统科学研究所
通讯作者Sun, Shaolong
作者单位1.Chinese Acad Sci, Ctr Forecasting Sci, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
4.Xi An Jiao Tong Univ, Sch Management, Xian, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Erlong,Du, Pei,Azaglo, Ernest Young,et al. Forecasting daily tourism volume: a hybrid approach with CEMMDAN and multi-kernel adaptive ensemble[J]. CURRENT ISSUES IN TOURISM,2022:20.
APA Zhao, Erlong,Du, Pei,Azaglo, Ernest Young,Wang, Shouyang,&Sun, Shaolong.(2022).Forecasting daily tourism volume: a hybrid approach with CEMMDAN and multi-kernel adaptive ensemble.CURRENT ISSUES IN TOURISM,20.
MLA Zhao, Erlong,et al."Forecasting daily tourism volume: a hybrid approach with CEMMDAN and multi-kernel adaptive ensemble".CURRENT ISSUES IN TOURISM (2022):20.

入库方式： OAI收割

来源：数学与系统科学研究院

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