Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition

Abstract In this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integrated moving average (ARIMA), with a nonlinear autoregressive artificial neural network (NARANN), called the EEMD-ARIMA-NARANN model, to perfo...

Description complète

Enregistré dans:
Détails bibliographiques
Auteurs principaux: Yongbin Wang, Chunjie Xu, Sanqiao Yao, Lei Wang, Yingzheng Zhao, Jingchao Ren, Yuchun Li
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
R
Q
Accès en ligne:https://doaj.org/article/81246b99e6dd45e79b01533e11f663f3
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!