  Millions of people have been infected and lakhs of people have lost their
lives due to the worldwide ongoing novel Coronavirus (COVID-19) pandemic. It is
of utmost importance to identify the future infected cases and the virus spread
rate for advance preparation in the healthcare services to avoid deaths.
Accurately forecasting the spread of COVID-19 is an analytical and challenging
real-world problem to the research community. Therefore, we use day level
information of COVID-19 spread for cumulative cases from whole world and 10
mostly affected countries; US, Spain, Italy, France, Germany, Russia, Iran,
United Kingdom, Turkey, and India. We utilize the temporal data of coronavirus
spread from January 22, 2020 to May 20, 2020. We model the evolution of the
COVID-19 outbreak, and perform prediction using ARIMA and Prophet time series
forecasting models. Effectiveness of the models are evaluated based on the mean
absolute error, root mean square error, root relative squared error, and mean
absolute percentage error. Our analysis can help in understanding the trends of
the disease outbreak, and provide epidemiological stage information of adopted
countries. Our investigations show that ARIMA model is more effective for
forecasting COVID-19 prevalence. The forecasting results have potential to
assist governments to plan policies to contain the spread of the virus.
