Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis

Tanujit Chakraborty; Indrajit Ghosh

Five time series COVID-19 datasets for Canada, France, India, South Korea, and the UK are considered for training the proposed model and the component models. The datasets are nonlinear, nonstationary, and non-gaussian in nature. We have used root mean square error (RMSE), mean absolute error (MAE), to evaluate the predictive performance of the models used in this study [17] . Since the number of data points in both the datasets is limited thus going for advanced deep learning techniques will simply over-fit the datasets [14] . We start the experimental evaluation for all the five datasets with the classical ARIMA(p,d,q) using 'forecast' [16] statistical package in R software. To fit an ARIMA model, we first specify the parameters of the model. Using ACF plot and PACF plot (See Table 1 ), we can decide the value of the parameters of the model. We have also performed unit root tests for stationarity check and all the datasets were found nonstationary. The 'best' fitted ARIMA model is chosen using AIC and BIC values for each training dataset. The fitted ARIMA models for five datasets are as follows: ARIMA(1,2,1) for India, ARIMA(1,1,2) for Canada, ARIMA(0,1,1) for France, ARIMA(2,1,0) for South Korea, and ARIMA(2,2,2) for the UK. We employ a pre-defined Box-Cox transformation set to λ = 0 to ensure the forecast values stay positive. As the ARIMA model is fitted, forecasts are generated for 10-time steps (5 April 2020 to 14 April 2020) for all the five datasets. We also compute training data predicted values and calculate the residual errors. Plots for the residual series are given in Figure 1 .