计算机工程与应用2012，Vol.48Issue(4)：225-227,3.DOI:10.3778/j.issn.1002-8331.2012.04.066
基于EMD和SVR的混合智能预测模型及实证研究
Hybrid intelligent prediction method based on EMD and SVR and its application
摘要
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.关键词
时间序列/经验模式分解/支持向量回归/预测Key words
time series/ empirical mode decomposition/ support vector regression/ forecasting分类
信息技术与安全科学引用本文复制引用
王巍,赵宏,梁朝晖,马涛..基于EMD和SVR的混合智能预测模型及实证研究[J].计算机工程与应用,2012,48(4):225-227,3.基金项目
国家自然科学基金(No.70971098) （No.70971098）
国家软科学研究计划项目(No.2011GXS2D015) （No.2011GXS2D015）
天津市哲学社会科学规划项目(No.TJYYl 1-2-042). （No.TJYYl 1-2-042）
