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ARIMA MODELS TO FORECAST COVID-19 IN KINGDOM OF SAUDI ARABIA DURING THE INTERVAL (1-2021 TO 1-2022) WEEKLY USING E-VIEWS PROGRAM
Keywords:
deaths, injuries, number of coronavirus infections, COVID-19, weekly infection rate, time series models, forecasting, E-Views program.DOI:
https://doi.org/10.17654/0972361722085Abstract
The main objective of this research is to analyze the fundamental differences in the basic indicators of the emerging corona virus, COVID-19, especially the number of total cases and the number of deaths resulting from it in Kingdom of Saudi Arabia, in order to evaluate the precautionary measures taken by KSA.
In this research, time series models were studied to predict the number of cases infected with COVID-19 that can be expected weekly in KSA during a period spanning a whole year using the numbers of weekly infections (WC) in KSA during the period from January 2021 to January 2022. The future values of injuries and deaths resulting from them were predicted using the time series method according to the current and previous values, and the E-Views statistical software package was used, which was specifically designed to process time series data.
The study proved that there were statistically significant differences in the number of weekly infections with the corona virus, in addition to the presence of statistically significant differences in the number of weekly deaths resulting from the corona virus in Kingdom of Saudi Arabia. The study also demonstrated the existence of a statistically significant correlation between the number of weekly infections with the corona virus and the deaths resulting from it in Kingdom of Saudi Arabia.
The automatic regression integrated moving average (ARIMA) model was used as one of the time series prediction methods and the prediction procedures were determined using the ARIMA model. The results of the analysis showed that the ARIMA(1, 2, 0) model gave the best results for prediction and data analysis. It is highly advised to maintain the social distancing with all safety measures.
Received: September 5, 2022
Accepted: October 1, 2022
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