Employment of GARCH Model and L.S.M.E Method in Time Series with Application to COVID-19 Virus

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Aaedah A. Mohammad, Nihad S. Khalaf‏ Aljboori


     This paper is concerned with approximating the short-term spread of the COVID-19 virus at the country from January 1, 2021 to July 25, 2022, extracted from the approved website of the Ministry of Health in Iraq using GARCH models and L.S.M.E method. In addition, we will try to determine the hypothetical inflection point and the final volume of cases of COVID-19.

      Where the analysis is from two parts firstly by applying the conditions for stability to GARCH models, and secondly part in the application of the Long-Short terms memory network , and we have found that the best model according to AIC and BIC information criteria is GARCH (1,0) this is regarding data analysis using GARCH  Models, while the analysis using L.S.M.E according to the same AIC and BIC criteria. The results are of a standard value less than GARCH models due to the lack of high fluctuation in the severity of the contrast, where the GARCH models are used for states of high fluctuation and insecure in the variation of error, due to the adaptive nature of the network models Nervousness made it preferred models to describe these data and get the lowest medium for errors.

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