On Hybrid Autoregressive Integrated Moving Average Artificial Neural Network Time Series Analysis of the Nigeria External Reserves

Abstract

The amount of the external reserves possessed by a country is an essential component in the measure of its economic status. In essence, a country with substantial amount of money in its external reserves would experience a buoyant and friendly economy among other nations. This study employs three different time series models to examine the status of the Nigeria External reserves for 22 years (1960-2022). These include the Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANN) and the hybrid ARIMA-ANN models. ARIMA (1,0,1)(0,0,2)12 emerged as the optimum model among other fitted linear models, having the smallest Akaike Information Criterion (AIC) value of 14365.84 and was used to fit the nonlinear Hybrid ARIMA-ANN model and for the prediction of the External Reserves series. The Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) values of (590.1479), (358.3421), and (24.10321) for the Hybrid ARIMA-ANN model were also the least in comparison with their corresponding values for the other models. These show that the Hybrid model has the least error value compared with those of the independent ARIMA and ANN models. Hence, the nonlinear Hybrid ARIMA-ANN model performed excellently in the estimation and the generation of the forecast values than the conventional linear ARIMA and the nonlinear ANN models.