Using Time Series Models to Predict the Numbers of People Afflicted with (COVID-19) in Iraq, Saudi Arabia and United Arab Emirates
DOI:
https://doi.org/10.29304/jqcm.2020.12.4.729Keywords:
Box- Jenkins models, ARIMA model, Coronavirus, time series analysisAbstract
Covid-19 disease is an infectious disease caused by the newly discovered Coronavirus. There was no knowledge of this virus before an outbreak broke out in the Chinese city of Yuhan in December 2019. The Corona epidemic has caused the world to go through a major challenge as it has claimed the lives of many people and also disrupted the economy in most countries of the world. This has prompted many researchers in various disciplines to conduct studies and research to stand in the face of this epidemic. It is known that statistical methods have great importance for all sciences The other that stood against this epidemic.In this paper, we use time series ARIMA models by Box- Jenkins to predict the numbers of people afflicted with (COVID-19) in Iraq, Saudi Arabia and United Arab Emirates and compare them based on a daily time series represent the numbers of people afflicted in those countries for the period from 3/15/2020 to 4/5/2020 the emergence of that epidemic in those countries.
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References
[2] Box G, E.P & Jenkins, G. M.,(1976), "Time series analysis forcasting and control sanfrancisco Helden-day".
[3] Brockwell, P.J. & Davis, R.A. (1987). Time series. Theory and methods, Springer.Cao, R. (1999). An overview of bootstrap methods for estimating and predicting in time series, Test, 8, 1, 95-116.
[4] Chatfield, (1975), "The analysis of time series, An introduction" second edition chapman and hall , London-New york
[5] Cryer, J.D., and Chan, K.S. (2008), "Time Series Analysis With Applications in R", 2nd ed., Springer, New York
[6] Fan,J.,and Y., Qiwei.(2003) .Nonlinear Time Series Nonparametric and Parametric Methods, "Springer, New York,USA".
[7] McLeod, A.I. & Li, W.K. (1983). Diagnostic checking ARMA time series models using squared-residual autocorrelations, Journal of Time Series Analysis, 17, 571—599.
