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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Detalles Bibliográficos
Autores principales: Zhang, Rui, Guo, Zhen, Meng, Yujie, Wang, Songwang, Li, Shaoqiong, Niu, Ran, Wang, Yu, Guo, Qing, Li, Yonghong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201362/
https://www.ncbi.nlm.nih.gov/pubmed/34200378
http://dx.doi.org/10.3390/ijerph18116174

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201362/
https://www.ncbi.nlm.nih.gov/pubmed/34200378
http://dx.doi.org/10.3390/ijerph18116174

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