Using Support Vector Machines to Predict Global Food Price Index

This research aims to Using Support Vector Machines (SVM) to predict global food price index, during the period from January 1990 to August 2020. The SVM model, with (Cost(C) = 10000, Epsilon(ε) = 0.1, gamma(γ) =90) has the lowest value of training error, with a small number of support vectors, is the best fit for monthly food price index predicting among all other SVM models with different parameter values.