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A statistical approach and analysis computing based on autoregressive integrated moving averages models to predict COVID-19 outbreak in Iraq | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 116، دوره 13، شماره 1، خرداد 2022، صفحه 1391-1415 اصل مقاله (725.5 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.5745 | ||
| نویسندگان | ||
| Ali Abdul Karim Kazem Naji* 1؛ Asmaa Shaker Ashoor2 | ||
| 1College of Education for Pure Science, University of Babylon, Iraq | ||
| 2College of Basic Education, University of Babylon, Iraq | ||
| تاریخ دریافت: 03 خرداد 1400، تاریخ بازنگری: 14 شهریور 1400، تاریخ پذیرش: 21 مهر 1400 | ||
| چکیده | ||
| A time series has been adopted for the numbers of people infected with the Covid-19 pandemic in Iraq for a whole year, starting from the first infection recorded on February 18, 2020 until the end of February 2021, which was collected in the form of weekly observations and at a size of 53 observations. The study found the quality and suitability of the autoregressive moving average model from order (1,3) among a group of autoregressive moving average models. This model was built according to the diagnostic criteria. These criteria are the Akaike information criterion, Bayesian Information Criterion, and Hannan \& Quinn Criterion models. The study concluded that this model from order (1,3) is good and appropriate, and its predictions can be adopted in making decisions. | ||
| کلیدواژه‌ها | ||
| Autoregressive Models؛ ACF؛ PACF؛ COVID-19؛ Unit Root Test | ||
| مراجع | ||
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