Predicting COVID-19 cases using bidirectional LSTM on multivariate time series

To assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short...

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Detalles Bibliográficos
Autores principales: Said, Ahmed Ben, Erradi, Abdelkarim, Aly, Hussein Ahmed, Mohamed, Abdelmonem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155803/
https://www.ncbi.nlm.nih.gov/pubmed/34043172
http://dx.doi.org/10.1007/s11356-021-14286-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155803/
https://www.ncbi.nlm.nih.gov/pubmed/34043172
http://dx.doi.org/10.1007/s11356-021-14286-7

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