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.
Application of auto-regressive time series model to marginal oil field production in Nigeria