Jiang-Ning Li, Xian-Liang Shi, An-Qiang Huang, Ze-Fang He, Yu-Xuan Kang, Dong Li
School of Economics and Management, Beijing Jiaotong University, Beijing, 100044 China.
National Medical Products Administration of China, Beijing, 100037 China.
Complex Intell Systems. 2023;9(3):2285-2295. doi: 10.1007/s40747-021-00289-x. Epub 2021 Mar 2.
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.
准确预测是应急医学储备管理的一项基础性和前沿性工作。鉴于在突发公共卫生事件期间，应急医学储备需求受到多种因素影响，观测数据由不同但难以区分的成分组成，传统的需求预测方法无法胜任这种情况。为弥补这一差距，本文提出了EMD-ELMAN-ARIMA（ELA）模型，该模型首先利用经验模态分解（EMD）将原始序列分解为各种成分。采用埃尔曼神经网络和ARIMA模型对识别出的成分进行预测，并通过整合各个成分的预测结果生成最终预测值。为进行验证，基于2014年至2018年北京的流感数据开展了实证研究。结果清楚地表明，所提出的ELA算法优于包括ARIMA和ELMAN模型在内的两个竞争对手。
