Prediction of fluctuation range and change trendof cotton price based on fuzzy informationgranulation and PSO-SVR model

Accurately predicting the prices of agricultural products is very important for evading market risk，increasing agricultural income and government macroeconomic regulation. With national cotton prices prediction asan example，this paper proposed a SVM prediction model of agricultural products prices based on fuzzy informationgranulation and particle swarm optimization algorithm. Firstly，the original national cotton prices A index time seriesdata were transformed into fuzzy information granulation particles made up of Low，R and Up. Secondly，particle swarmoptimization algorithm was used to find the best parameters c and g for SVM model. Finally，cotton price fluctuation rangeand change trend in the future were predicted by the optimized SVM regression model. The empirical analysis showedthat the PSO-SVM model was effective for the prediction of cotton price fluctuation range and change trend.