ABSTRACT
Fast spreading coronavirus disease 2019 (COVID-19), originated in the Wuhan city, China in December 2019, is a contagious disease caused by Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2). Within a short period, COVID-19 infections spread over large geographical area affecting millions of people and declared a pandemic by the World Health Organization (WHO). The fast and quick spread of the virus across the globe resulted in thousands of casualties. COVID-19 prevalence in India was reported in the late of January 2020 and the number of infections increased sharply by the end of March. In such a troublesome situation, time series analysis proves very much helpful in monitoring and assessing the growth curve of COVID-19 infections. In the present study, autoregressive integrated moving average (ARIMA) models are developed for the time series data of cumulative confirmed, recovered and causalities cases of COVID-19 in India. The data set under study is broken up into two subsets, modelling and testing data sets. After analysing the input data for stationarity using autocorrelation function (ACF) and partial correlation function (PACF) plots, different ARIMA models are estimated for confirmed, recovered and causalities’ cases of COVID-19 in India for modelling phase. ARIMA Model outputs are then compared with observed values of confirmed, recovered and casualties’ cases for the testing phase using error analysis. It has been found that ARIMA (0, 2, 3 ), ARIMA (0, 2, 5 ) and ARIMA (1, 2, 1 ) models are appropriate with the lowest mean absolute percentage error (MAPE) values for the data of confirmed cases, recovered cases and casualties’ cases respectively. Finally, the developed ARIMA models are used to forecast one-month ahead values of confirmed, recovered and casualties’ cases of COVID-19 in India. The predictions indicate rise in confirmed COVID-19 cases and speedy recoveries as well, whereas the casualties continue to show a constant trend in future. Based on these future trends of COVID-19 outbreak, governments and policymakers can take preventive measures to break the ongoing chain of COVID-19 infections and make necessary arrangements in the wake of an emergency. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.