Türkiye's Egg Export to Iraq: Performance Comparison of Seasonal ARIMA and Artificial Neural Network Models

This study aims to identify the most effective model for predicting the monthly export volumes of eggs from Türkiye to Iraq by comparing two primary forecasting methods: the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and the Artificial Neural Network (ANN) model. Both models were applied to monthly export data of egg products from 2010 to 2020, sourced from reliable databases such as the UN Comtrade and Turkish Statistical Institute (TURKSTAT). The performance of both models was assessed using key statistical metrics, including the Akaike Information Criterion (AIC), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). According to the results, the Feed-Forward Neural Networks (FFNN) model demonstrated superior predictive accuracy compared to the SARIMA model. This conclusion is supported by the FFNN model’s lower MAE, RMSE, and AIC values, indicating fewer forecasting errors and a better overall fit to the data. Therefore, the study concludes that the FFNN model is more effective and accurate than the SARIMA in predicting the export values of eggs from Türkiye to Iraq.