Application of ARIMA and Holt-Winters forecasting model to predict the spreading of COVID-19 for India and its states

Panda, M.

The novel Corona-virus (COVID-2019) epidemic has posed a global threat to human life and society. The whole world is working relentlessly to find some solutions to fight against this deadly virus to reduce the number of deaths. Strategic planning with predictive modelling and short term forecasting for analyzing the situations based on the worldwide available data allow us to realize the future exponential behaviour of the COVID-19 disease. Time series forecasting plays a vital role in developing an efficient forecasting model for a future prediction about the spread of this contagious disease. In this paper, the ARIMA (Auto regression integrated moving average) and Holt-Winters time series exponential smoothing are used to develop an efficient 20- days ahead short-term forecast model to predict the effect of COVID-19 epidemic. The modelling and forecasting are done with the publicly available dataset from Kaggle as a perspective to India and its five states such as Odisha, Delhi, Maharashtra, Karnataka, Andhra Pradesh and West Bengal. The model is assessed with correlogram, ADF test, AIC and MAPE to understand the accuracy of the proposed forecasting model.