Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China

OBJECTIVES: This study intends to build and compare two kinds of forecasting models at different time scales for hemorrhagic fever incidence in China. METHODS: Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) were adopted to fit monthly, weekly and da...

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Detalles Bibliográficos
Autores principales: Zhang, Rui, Song, Hejia, Chen, Qiulan, Wang, Yu, Wang, Songwang, Li, Yonghong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759700/
https://www.ncbi.nlm.nih.gov/pubmed/35030203
http://dx.doi.org/10.1371/journal.pone.0262009

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759700/
https://www.ncbi.nlm.nih.gov/pubmed/35030203
http://dx.doi.org/10.1371/journal.pone.0262009

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