Forecasting daily confirmed COVID-19 cases in Algeria using ARIMA models

Abstract

Coronavirus disease has become a worldwide threat affecting almost every country in the world. The aim of this study is to identify the COVID-19 cases (positive, recovery and death) in Algeria using the Double Exponential Smoothing Method and an Autoregressive Integrated Moving Average (ARIMA) model for forecasting the COVID-19 cases.The data for this study were obtained from March 21st, 2020 to November 26th, 2020. The daily Algerian COVID-19 confirmed cases were sourced from The Ministry of Health, Population and Hospital Reform of Algeria. Based on the results of PACF, ACF, and estimated parameters of the ARIMA model in the COVID-19 case in Algeria following the ARIMA model (0,1,1). Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria.

Predicting the impact of the third wave of COVID-19 in India using hybrid statistical machine learning models: A time series forecasting and sentiment analysis approach

“…The results generated by this model were accurate after validation of the model. The projected COVID-19 cases confirmed that the recovered cases and deaths followed an exact pattern during the three days examined [ 19 ].…”