关键词: 时间序列, 经验模式分解, 支持向量回归, 预测

Abstract: Due to the fluctuation and complexity of non-linear and non-stationary time series, it is difficult to use a single forecasting method to accurately describe the moving tendency. So a novel hybrid intelligent forecasting model based on Empirical Mode Decomposition（EMD） and Support Vector Regression（SVR） is proposed, where these Intrinsic Mode Functions（IMF） are adaptively extracted via EMD from a non-stationary time series according to the intrinsic characteristic time scales. Tendencies of these IMF are forecasted with SVR respectively, in which the kernel functions are appropriately chosen with these different fluctuations of IMF. These forecasting results of IMF are combined to output the forecasting result of the original time series. The proposed model is applied to the tendency forecasting of non-linear and non-stationary time series, and the results show that the forecasting performance of the hybrid model outperforms SVR with the single-step ahead forecasting or the multi-step ahead forecasting.

Key words: time series, empirical mode decomposition, support vector regression, forecasting