Hybrid intelligent forecasting model based on empirical mode decomposition, support vector regression and adaptive linear neural network

Authors:
Zhengjia He;Qiao Hu;Yanyang Zi;Zhousuo Zhang;Xuefeng Chen
Affiliations:
The State Key Laboratory for Manufacturing System, Department of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;The State Key Laboratory for Manufacturing System, Department of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;The State Key Laboratory for Manufacturing System, Department of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;The State Key Laboratory for Manufacturing System, Department of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;The State Key Laboratory for Manufacturing System, Department of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, P.R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

In this paper, a novel hybrid intelligent forecasting model based on empirical mode decomposition (EMD), support vector regression (SVR) and adaptive linear neural network (ALNN) is proposed, where these intrinsic mode components (IMCs) are adaptively extracted via EMD from a nonstationary time series according to the intrinsic characteristic time scales. Tendencies of these IMCs are forecasted with SVR respectively, in which kernel functions are appropriately chosen with these different fluctuations of IMCs. These forecasting results of IMCs are combinated with ALNN to output the forecasting result of the original time series. The proposed model is applied to the tendency forecasting of the Mackey-Glass benchmark time series and a vibration signal from a machine set. Testing results show that the forecasting performance of this proposed model outperforms that of the single SVR method under single-step ahead forecasting or multi-step ahead forecasting.