KMS Of Academy of mathematics and systems sciences, CAS
| Forecasting daily tourism volume: a hybrid approach with CEMMDAN and multi-kernel adaptive ensemble | |
Zhao, Erlong1; Du, Pei1; Azaglo, Ernest Young1; Wang, Shouyang2,3,4 ; Sun, Shaolong1
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| 2022-03-24 | |
| Source Publication | CURRENT ISSUES IN TOURISM
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| ISSN | 1368-3500 |
| Pages | 20 |
| Abstract | 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. |
| Keyword | Daily tourism volume forecasting decomposition ensemble approach sample entropy kernel extreme learning machine multi-kernel adaptive strategy |
| DOI | 10.1080/13683500.2022.2048806 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[72101197] ; National Natural Science Foundation of China[71988101] ; Fundamental Research Funds for the Central Universities[SK2021007] |
| WOS Research Area | Social Sciences - Other Topics |
| WOS Subject | Hospitality, Leisure, Sport & Tourism |
| WOS ID | WOS:000772339300001 |
| Publisher | ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/60176 |
| Collection | 系统科学研究所 |
| Corresponding Author | Sun, Shaolong |
| Affiliation | 1.Xi An Jiao Tong Univ, Sch Management, Xian, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China 4.Chinese Acad Sci, Ctr Forecasting Sci, Beijing, Peoples R China |
| Recommended Citation 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. |
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