On Predicting Growth Factor Data of Covid-19 Epidemic Using Hybrid Arima-Ann Model

Abstract

The Autoregressive Integrated Moving Average (ARIMA) model cannot capture the nonlinear patterns exhibited by the 2019 coronavirus (COVID-19) in terms of daily growth factor. As a result, Artificial Neural Networks (ANNs) and Hybrid ARIMA-ANN models have been successfully applied to resolve problems with nonlinear estimation. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data. The best forecasting model selected was compared using the forecasting assessment criterion known as mean absolute error. The main finding results show that the ANN model is more efficient than the ARIMA and Hybrid ARIMA-ANN models. The main finding from the ANN model analysis indicates that the magnitude of the increase in growth factor over time is rising in general while the percentage change in the growth factor is declining. This may be the result of the social distancing, safety, and cautionary measures mandated by governments worldwide.