大气和海洋科学快报（英文版）2021，Vol.14Issue(4)：52-57,6.
基于集合经验模态分解和自回归-移动平均模型的COVID-19流行病全球预测系统预测结果改进
Improvement of the global prediction system of the COVID-19 pandemic based on the ensemble empirical mode decomposition(EEMD)and autoregressive moving average(ARMA)model in a hybrid approach
摘要
Abstract
In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic(GPCP).In this article,the authors use the ensemble empirical mode decomposition(EEMD)model and autoregressive moving average(ARMA)model to improve the prediction results of GPCP.In addition,the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease,whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model.Judging from the results,the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP.For countries such as El Salvador with a small number of cases,the absolute values of the relative errors of prediction become smaller.Therefore,this article concludes that this method is more effective for improving prediction results and direct prediction.关键词
COVID-19/预测/EEMD-ARMA混合方法/历史数据Key words
COVID-19/prediction/hybrid EEMDARMA method/historical data引用本文复制引用
Chuwei Liu,Jianping Huang,Fei Ji,Li Zhang,Xiaoyue Liu,Yun Wei,Xinbo Lian..基于集合经验模态分解和自回归-移动平均模型的COVID-19流行病全球预测系统预测结果改进[J].大气和海洋科学快报（英文版）,2021,14(4):52-57,6.基金项目
This work was jointly supported by the National Natural Science Foundation of China[grant numbers 41521004 and 41875083]and the Gansu Provincial Special Fund Project for Guiding Scientific and Tech-nological Innovation and Development[grant number 2019ZX-06]. （）
