Affiliations:
Department of Mathematics, College of Science and Arts, King Abdulaziz University, Jeddah, Saudi Arabia
This study explores the use of the autoregressive integrated moving average (ARIMA) data-driven modeling approach for forecasting peanut yields in Sudan. Two tests were conducted: one using the original dataset and another using accumulated data. The main objective was to improve forecasting accuracy by applying a method that incorporates accumulated data for future predictions. The results, based on a comparison of the two tests, indicate that the proposed approach enhances prediction clarity. Model identification showed an increase in the coefficient of determination, a decrease in the Bayesian information criterion (BIC), and a reduction in the mean absolute error. These outcomes suggest that the proposed method may provide more accurate forecasts and could be useful for forecasting in various fields.
Forecasting accuracy, ARIMA model, Accumulated data, Time series, Model evaluation
https://doi.org/10.21833/ijaas.2025.10.021
Elhafian, M. H., Hussien, H. H., Sidahmed, A. O. M., & Aljifri, M. (2025). The effect of an accumulation algorithm on the predictive accuracy of ARIMA models. International Journal of Advanced and Applied Sciences, 12(10), 203–215. https://doi.org/10.21833/ijaas.2025.10.021