关键词: 差分自回归移动平均, 支持向量机, 组合预测, 国际铀资源价格

Abstract: The traditional model can not capture uranium resource price’s comprehensive trend，because of unsteady and nonlinear time-series data.The forecasting model based on the combination of Autoregressive Integrated Moving Average（ARIMA） and Support Vector Machine（SVM） is built to improve the prediction accuracy of the model in this paper and PSO algorithm is used to optimize the parameters of SVM.The proposed model is applied to uranium resource price tendency forecasting example，and the simulation results show that the forecasting performance of the hybrid model outperforms single ARIMA and SVM ahead forecasting.

Key words: Autoregressive Integrated Moving Average（ARIMA）, Support Vector Machine（SVM）, combination forecasting, uranium resource price