A Comparison of ARIMA and GVM(1,1) models for Forecasting Rice Production in Iraq

Abstract

Time series forecasting is  a statistical method, involves analyzing past data patterns to predict future values. It is widely applied in various fields. This research compared the forecasting performance between ARIMA model and the Grey Verhulst Model GVM(1,1) model to determine the best method to forecast the rice production in Iraq. The data was obtained from the “International Production Assessment Division (IPAD)” website. The performance of the two models will be evaluated using various metrics, such as root mean square error (RMSE) and mean absolute percentage error (MAPE). From the result of the forecasting accuracy based on RMSE and MAPE showed that the values of RMSE and MAPE of ARIMA (1,1,2)  model is smaller than the values of GVM(1,1). it can be concluded that the performance of ARIMA is better than GVM (1,1)  to forecast the rice production in Iraq.