Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms

In this paper, we establish daily confirmed infected cases prediction models for the time series data of America by applying both the long short-term memory (LSTM) and extreme gradient boosting (XGBoost) algorithms, and employ four performance parameters as MAE, MSE, RMSE, and MAPE to evaluate the e...

Descripción completa

Detalles Bibliográficos
Autores principales: Luo, Junling, Zhang, Zhongliang, Fu, Yao, Rao, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216863/
https://www.ncbi.nlm.nih.gov/pubmed/34178594
http://dx.doi.org/10.1016/j.rinp.2021.104462

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216863/
https://www.ncbi.nlm.nih.gov/pubmed/34178594
http://dx.doi.org/10.1016/j.rinp.2021.104462

Ejemplares similares