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Forecasting COVID-19 Time Series Based on an Autoregressive Model
Marques,Joao Alexandre Lobo1; Gois,Francisco Nauber Bernardo2; Xavier-Neto,José3; Fong,Simon James4
2021
Source PublicationSpringerBriefs in Applied Sciences and Technology
Pages41-54
AbstractWhen considering time-series forecasting, the application of autoregressive models is a popular and simple technique that is usually considered. In this chapter, we present the basic theoretical aspects and assumptions of the ARIMA—Autoregressive Integrated Moving Average model. It is considered for the prediction of the COVID-19 epidemiological data series of five different countries (China, United States, Brazil, Italy, and Singapore), each of them with specific curves, which are results of the virus reproduction itself but also of policies and government decisions during the pandemic spread. The discussion about the results is performed with the focus on the three evaluation criteria of the model: R Score, MAE, and MSE. Higher R Score was obtained when the sample time series was smoothly increasing or decreasing. The error metrics were higher when the prediction was performed for oscillating data series. This may indicate that the use of ARIMA models may be suitable as a prediction tool for the COVID-19 when the country is not facing severe oscillations in the number of infections.
DOI10.1007/978-3-030-61913-8_3
URLView the original
Language英语
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Document TypeBook chapter
CollectionUniversity of Macau
Corresponding AuthorMarques,Joao Alexandre Lobo
Affiliation1.Laboratory of Neuroapplications,University of Saint Joseph,Macao
2.Machine Learning Department,Secretary of Health of the Government of the State of Ceara,Fortaleza,Brazil
3.Government Intelligence Cell,Secretary of Health of the Government of the State of Ceara,Fortaleza,Brazil
4.Department of Computer and Information Science,University of Macau,Macao
Recommended Citation
GB/T 7714
Marques,Joao Alexandre Lobo,Gois,Francisco Nauber Bernardo,Xavier-Neto,José,et al. Forecasting COVID-19 Time Series Based on an Autoregressive Model,2021:41-54.
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