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

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...

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Main Authors: Yongbin Wang, Chunjie Xu, Sanqiao Yao, Lei Wang, Yingzheng Zhao, Jingchao Ren, Yuchun Li
Format: article
Language:EN
Published: Nature Portfolio 2021
Subjects:
Medicine
R
Science
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Online Access:https://doaj.org/article/81246b99e6dd45e79b01533e11f663f3
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