A hybrid time-series forecasting model using extreme learning machines

摘要：

This study proposes a hybrid model which combines the linear autoregression(AR)with the nonlinear neural network(NN)based on the extreme learning machine(ELM) in an integral structure in order to improve the accuracy of time-series prediction. Unlike the developed hybrid forecasting models introduced in the literature,which usually treat the original forecasting models as a separate linear or nonlinear unit, the proposed hybrid model is an integrated model which can adapt well to both linear and non-linear situations often in periodical time series with a complicated structure.The hybrid algorithm is tested against different kinds of time series data and the results indicate that the hybrid algorithm outperforms the AR and the ELM-based neural network.

关键词： Time series Forecasting Extreme learning machines Autoregression

作者: F. Pan H. Zhang M. Xia

作者单位: School of Information Science and Technology,Dong Hua University,Shanghai, China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 933-936

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）