a flexible combination forecast method for modeling agricultural commodity prices: a case study iran’s ‎livestock and poultry meat market

In recent years, the fluctuation in agricultural commodity prices in iran is increased and thus, accurate forecasting of price change is necessary. in this article, a flexible combined method in modeling monthly prices of beef, lamb and chicken from april 2001 to march 2021, was proposed. in this new method, three different approaches namely simple averaging, discounted and shrinkage methods were effectively used to combine the forecasting outputs of three hybrid methods (mlpann-ga, mlpann-pso and mlpann-ica) together. in implementation stage of hybrid methods, based on test and error method, the optimal mlpann structure was found with 3/2/4–6–1 architectures and the controlling parameters are carefully assigned. the results obtained from three hybrid methods indicate that, based on the rmse statistical index, the mlpann-ica method performs the best when forecasting prices for beef, lamb, and chicken. the outputs of three combination approaches show that the shrinkage method, with a parameter value of k=0.25, achieves the highest prediction accuracy when forecasting prices for these three meats. in summary, the proposed method outperforms the other three hybrid methods overall.

a flexible combination forecast method for modeling agricultural commodity prices: a case study iran’s ‎livestock and poultry meat market

in recent years, the fluctuation in agricultural commodity prices in iran is increased and thus, accurate forecasting of price change is necessary. in this article, a flexible combined method in modeling monthly prices of beef, lamb and chicken from april 2001 to march 2021, was proposed. in this new method, three different approaches namely simple averaging, discounted and shrinkage methods were effectively used to combine the forecasting outputs of three hybrid methods (mlpann-ga, mlpann-pso and mlpann-ica) together. in implementation stage of hybrid methods, based on test and error method, the optimal mlpann structure was found with 3/2/4–6–1 architectures and the controlling parameters are carefully assigned. the results obtained from three hybrid methods indicate that, based on the rmse statistical index, the mlpann-ica method performs the best when forecasting prices for beef, lamb, and chicken. the outputs of three combination approaches show that the shrinkage method, with a parameter value of k=0.25, achieves the highest prediction accuracy when forecasting prices for these three meats. in summary, the proposed method outperforms the other three hybrid methods overall.