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Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China

Background: This study intends to identify the best model for predicting the incidence of hand, foot and mouth disease (HFMD) in Ningbo by comparing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) models combined and uncombined with exogenous meteoro...

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Detalhes bibliográficos
Publicado no:Int J Environ Res Public Health
Main Authors: Zhang, Rui, Guo, Zhen, Meng, Yujie, Wang, Songwang, Li, Shaoqiong, Niu, Ran, Wang, Yu, Guo, Qing, Li, Yonghong
Formato: Artigo
Idioma:Inglês
Publicado em: MDPI 2021
Assuntos:
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC8201362/
https://ncbi.nlm.nih.gov/pubmed/34200378
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/ijerph18116174
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