Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology
Abstract Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish co...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2021-03-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-021-00289-x |
| _version_ | 1797806462056005632 |
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| author | Li Jiang-ning Shi Xian-liang Huang An-qiang He Ze-fang Kang Yu-xuan Li Dong |
| author_facet | Li Jiang-ning Shi Xian-liang Huang An-qiang He Ze-fang Kang Yu-xuan Li Dong |
| author_sort | Li Jiang-ning |
| collection | DOAJ |
| description | Abstract Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models. |
| first_indexed | 2024-03-13T06:07:41Z |
| format | Article |
| id | doaj.art-bccbaf6d30f849df88394983875f85b8 |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-13T06:07:41Z |
| publishDate | 2021-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-bccbaf6d30f849df88394983875f85b82023-06-11T11:29:45ZengSpringerComplex & Intelligent Systems2199-45362198-60532021-03-01932285229510.1007/s40747-021-00289-xForecasting emergency medicine reserve demand with a novel decomposition-ensemble methodologyLi Jiang-ning0Shi Xian-liang1Huang An-qiang2He Ze-fang3Kang Yu-xuan4Li Dong5School of Economics and Management, Beijing Jiaotong UniversitySchool of Economics and Management, Beijing Jiaotong UniversitySchool of Economics and Management, Beijing Jiaotong UniversityBeijing Wuzi UniversitySchool of Economics and Management, Beijing Jiaotong UniversityUniversity of LiverpoolAbstract Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models.https://doi.org/10.1007/s40747-021-00289-xARIMAELMANEMDMedicine reservePublic health events |
| spellingShingle | Li Jiang-ning Shi Xian-liang Huang An-qiang He Ze-fang Kang Yu-xuan Li Dong Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology Complex & Intelligent Systems ARIMA ELMAN EMD Medicine reserve Public health events |
| title | Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology |
| title_full | Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology |
| title_fullStr | Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology |
| title_full_unstemmed | Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology |
| title_short | Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology |
| title_sort | forecasting emergency medicine reserve demand with a novel decomposition ensemble methodology |
| topic | ARIMA ELMAN EMD Medicine reserve Public health events |
| url | https://doi.org/10.1007/s40747-021-00289-x |
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