Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA).

ArunKumar, K E; Kalaga, Dinesh V; Sai Kumar, Ch Mohan; Chilkoor, Govinda; Kawaji, Masahiro; Brenza, Timothy M

ArunKumar, K E; Kalaga, Dinesh V; Sai Kumar, Ch Mohan; Chilkoor, Govinda; Kawaji, Masahiro; Brenza, Timothy M.

Afiliação

ArunKumar KE; Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA.
Kalaga DV; Mechanical Engineering Department, City College of New York, New York, NY 10031, USA.
Sai Kumar CM; Process Chemistry and Technology, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, UP, 226015, India.
Chilkoor G; Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology, Rapid City, SD, 57701, USA.
Kawaji M; Mechanical Engineering Department, City College of New York, New York, NY 10031, USA.
Brenza TM; Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA.

Appl Soft Comput ; 103: 107161, 2021 May.

Article em En | MEDLINE | ID: mdl-33584158

Palavras-chave

ARIMA; Akaike Information Criteria (AIC); Auto-Correlation Function (ACF); Bayesian Information Criterion (BIC); COVID-19; Pandemic; SARIMA; Statistical modeling; Time-series forecast

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Appl Soft Comput Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos

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