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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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[1] S. Bisgaard, M. Kulahci, Time Series Analysis and Forecasting By Example, John Wiley & Sons, Inc., Hoboken, New Jersey, (2011). [2] G. E. P. Box and G. M. Jenkins, Time series analysis forecasting and control, Helden-Day, ISBN 0-8162-11043, 2 revised, 1976. [3] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, G. M. Ljung, Time Series Analysis: Forecasting and Control, John Wiley and Sons Inc., Hoboken, New Jersey, 2016. [4] A. Hernandez-Matamoros, H. Fujita, T. Hayashi and H. Perez-Meana, Forecasting of COVID-19 per regions using ARIMA models and polynomial functions, Appl. Soft Comput., 96 (2020) 106610. [5] E. Ghysels, M. Marcellino, Applied economic forecasting using time series methods, Oxford University Press, 2018. [6] E. Kocenda, A. Cerny, Elements of Time Series Econometrics: An Applied Approach, Karolinum Press, Charles University, 2017. [7] M. S. Paolella, Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH. John Wiley & Sons, Series in Probability and Statistics, LCCN 2018032640 (ebook) | ISBN 9781119431855, 2018. [8] M. Panda, Application of ARIMA and Holt-Winters forecasting model to predict the spreading of COVID-19 for India and its states, medRxiv,( 2020), https://doi.org/10.1101/2020.07.14.20153908. [9] T. C. Mills, Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting, 1st Edition, Academic Press, 2019. [10] A. Nielsen, Practical Time Series Analysis: Prediction with Statistics and Machine Learning, O’Reilly Media, (2019). [11] M. H. D. Ribeiro, and others, Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives of Brazil, Chaos, Solitons and Fractals, 135(2020) 109853. [12] R. H. Shumway and D. S. Stoffer, Time Series Analysis and Its Applications: With R Examples, Springer Int. Publ., 2017. [13] E. T. Vieira, Introduction to Real World Statistics with Step-by-Step SPSS Instructions, Routledge, Taylor and Francis Group New York and London, 2017. [14] W. A. Woodward, H. L. Gray, A. C. Elliott, Applied Time Series Analysis with R, CRC Press, Taylor & Francis Group, LLC, ISBN 9781498734226 (pbk.) 2017. | ||
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