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

doi:10.1080/13683500.2022.2048806

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 系统科学研究所

	Forecasting daily tourism volume: a hybrid approach with CEMMDAN and multi-kernel adaptive ensemble
	Zhao, Erlong 1; Du, Pei 1; Azaglo, Ernest Young 1; Wang, Shouyang 2,3,4; Sun, Shaolong 1
刊名	CURRENT ISSUES IN TOURISM
	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
DOI	10.1080/13683500.2022.2048806
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
出版者	ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
WOS记录号	WOS:000772339300001
内容类型	期刊论文
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.Xi An Jiao Tong Univ, Sch Management, Xian, Peoples R China 2.Chinese Acad Sci, Ctr Forecasting Sci, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, 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.