Author(s): Amir Khaleel Hassoo
This research examines the use of ARIMA and BSTS models to predict potato output in Iraq from 1961 to 2022. The ARIMA (1,1,3) model was chosen for its capacity to capture the trend and sea-sonality of the time series. On the other hand, the (BSTS) model was used to analyze the factors that affect potato production. The model's performance and prediction accuracy were evaluated using key statistical measures such as the Akaike Information Criterion (AIC), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings suggest that both models successfully forecast potato production, with the ARIMA model showing substantial co-efficients and the BSTS model appropriately reflecting the inherent dependence structure. The projected values closely correspond to the actual production data, indicating the resilience of the models in agricultural forecasting. This study enhances the understanding of agricultural fore-casting by showcasing the effectiveness of sophisticated time series models in collecting and pre-dicting intricate production trends. The results have significant implications for agricultural pol-icymakers, stakeholders, and academics who are interested in efficiently allocating resources, improving food security, and promoting sustainable agricultural growth in Iraq. Potential areas for future study are enhancing models by including supplementary variables and expanding the analysis to encompass other agricultural commodities and geographies.