Implication of ARIMA Time Series Model on COVID-19 Outbreaks in India

This research paper focuses on a Time Series Model to predict COVID-19 Outbreaks in India. COVID-19 Corona virus disease has been recognized as a worldwide hazard, and most of the studies are being conducted using diverse mathematical techniques to forecast the probable evolution of this outbreak. These mathematical models based on various factors and analyses are subject to potential bias. Here, we put forward a natural Times Series (TS) model that could be very useful to predict the spread of COVID-19. Here, a popular method Auto Regressive Integrated Moving Average (ARIMA) TS model is performed on the real COVID-19 data set to predict the outbreak trend of the prevalence and incidence of COVID-19 in India. Every day data of fresh COVID-19 confirmed cases act as an exogenous factor in this frame. Our data envelops the time period from 12th March, 2020 to 27th June, 2020. The time series under study is a non-stationary. Results obtained in the study revealed that the ARIMA model has a strong potential for prediction. In ACF and PACF graphs. Lag 1 and Lag 40 was found to be significant. Regressed values imply Lag 1 and Lag 40 was significant in predicting the present trend. The model predicted maximum COVID-19 cases in India at around 14, 22,337 with an interval (12, 80,352 – 15, 69, 817) during 1st July to 30th July period cumulatively. As per the model, the number of new cases shall increases drastically in India only. The results will help governments to make necessary arrangements as per the estimated cases. This kind of investigation, implications of ARIMA models and fitting procedures are useful in forecasting COVID-19 Outbreaks in India.