A Time Series Forecasting for the Cumulative Confirmed and Critical Cases of the Covid-19 Pandemic in Saudi Arabia using Autoregressive Integrated Moving Average (ARIMA) Model

Abstract

Reviews at present, different machine learning techniques and algorithms have been applied for predicting significant factors of the Coronavirus Disease-2019 (COVID-19) such as the outbreak and diagnosis. In this study, the most accurate time series forecasting model, namely, the Autoregressive Integrated Moving Average (ARIMA) model is used to forecast the expected cumulative number of confirmed and critical cases in Saudi Arabia for the upcoming months. Additionally, the dataset is collected from the King Abdullah Petroleum Studies and Research Centre (KAPSARC). Acquiring the number of expected cases within a short period is considered crucial as it provides an important knowledge that can be applied by the health sector in containing the COVID-19 pandemic and forming the proper precautions and strategies that are concerned on the public health system. The main finding of this research is that the number of cumulative confirmed cases is expected to increase at a high rate in the upcoming two months, while the number of critical cases is forecasted to increase at a smaller rate compared to the total number of cases. To evaluate the performance of the adopted model, different statistical matrices as the R Squared, Mean Squarer Error, Root Mean Square Error and Mean Absolute Error are used in this research. It is found to be proven from the findings that the proposed model generates an accurate prediction of the expected number of cumulative confirmed and critical cases in the upcoming months.