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

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 perform time s...

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Detalles Bibliográficos
Autores principales: Wang, Yongbin, Xu, Chunjie, Yao, Sanqiao, Wang, Lei, Zhao, Yingzheng, Ren, Jingchao, Li, Yuchun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8560776/
https://www.ncbi.nlm.nih.gov/pubmed/34725416
http://dx.doi.org/10.1038/s41598-021-00948-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8560776/
https://www.ncbi.nlm.nih.gov/pubmed/34725416
http://dx.doi.org/10.1038/s41598-021-00948-6

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