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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
The Author(s). Published by Elsevier B.V.
2021
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| Materias: | |
| 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 |