Evaluation of the Performance of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) Models: Application to Confirmed Cases of Covid-19 in Nigeria
Olafioye, Sunday Omotola
Department of Statistics, Faculty of Science, University of Abuja, Abuja, Nigeria.
Oguntade, Emmanuel Segun
Department of Statistics, Faculty of Science, University of Abuja, Abuja, Nigeria.
Awogbemi, Clement Adeyeye *
Department of Statistics, National Mathematical Centre, Abuja, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
In this study, the performance of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) models was investigated and evaluated using daily confirmed cases of COVID-19 in Nigeria. The stationarity status of the data collected was established using Augmented Dickey Fuller unit root test. The residual normality test was also carried out with the residual plots indicating adequacy of the fitted ARIMA model. The results of neural networks were analyzed using back-propagation for multilayer feed-forward powered by sigmoid function. Utilizing backpropagation method based on three factors expressed in terms of the learning rate, the distance between the actual output and predicted output and the activation function, the network weights were generated The performance indices for ARIMA and ANNs models were evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE and the results revealed that the ARIMA model performed better than the ANN model considering the minimum prediction error and forecasting ability. The ARIMA (2, 1, 1) model appeared to be the best fitted model over the ANN model for the daily confirmed covid-19 cases considered.
Keywords: Coronavirus, performance evaluation, stationarity test, ARIMA models, artificial neural networks