Comparison Between a Composite Model and Artificial Neural Network for Forecasting Imports in Malaysia

Abstract

This study aims to develop a suitable model for forecasting Malaysian imports within the studied sample. Imports are a key indicator that provides a clear picture of a country's macroeconomic situation, as defined by international bodies such as the World Bank. Abstract. This study aims to develop a suitable model for forecasting Malaysian imports within the studied sample. Imports are a key indicator that provides a clear picture of a country's macroeconomic situation, as defined by international bodies such as the World Bank. This study used the composite (a combined regression–ARIMA) model and artificial neural network (ANN) models proposed in this research.   thereby facilitating the development of a more effective   framework for forecasting the volume of Malaysia’s imports and enhancing existing prediction methods. The prediction performance is assessed using the Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results show that the combined model outperforms artificial neural network models in terms of accuracy. Its primary strengths lie in its predictive power and its ability to address regression model issues such as residual autocorrelation, by providing structural and time series explanations for those parts of the variance. Model parameters and predictions were estimated using 52 observations. Future research may expand on these findings by exploring other approaches, such as combined models that account for autocorrelation or heterogeneity problems, or by applying larger datasets on Malaysia’s imports and comparing the results with those of the present study.