%0 Journal Article
%T A New Direct Multi-step Ahead Prediction Model Based on EMD and Chaos Analysis<br>基于经验模式分解与混沌分析的直接多步预测模型
%A XIE Jing-Xin
%A CHENG Chun-Tian
%A ZHOU Gui-Hong
%A SUN Yu-Mei
%A <br>谢景新
%A 程春田
%A 周桂红
%A 孙玉梅
%J 自动化学报
%D 2008
%I 
%X The direct multi-step ahead prediction model, which employs observation values and does not depend on the result of single-step prediction, provides more accurate prediction than indirect model. But in this case, the model could be asked to learn various object functions. In this paper, a hybrid model is presented based on empirical mode decomposition (EMD) and chaos analysis. The model employs EMD to decompose the original sequences into many basic modal partitions which can significantly represent potential information of original time series. And chaos features of those data sequences can be used to design DRNN. By these means, the model can be improved to learn various objective functions. And then, more precious prediction can be obtained. Finally, a benchmark time series is tested to display the advantage of this model.
%K Direct multi-step ahead prediction
%K empirical mode decomposition
%K chaos analysis<br>直接多步预测
%K 经验模式分解
%K 混沌分析
%K 经验模式分解
%K 混沌分析
%K 预测模型
%K Analysis
%K Chaos
%K Based
%K Prediction
%K Model
%K Ahead
%K 验证
%K 时间序列分解
%K 基准
%K 预测精度
%K 能力
%K 差别
%K 组合预测
%K 神经网络
%K 基本模式分量
%K 尺度
%K 分解方法
%K 使用模式
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=95395DA1D9FCDBFE4FDAFF7E946C5667&yid=67289AFF6305E306&vid=339D79302DF62549&iid=B31275AF3241DB2D&sid=47122BA7BE181C5A&eid=128B7AEF80A42C95&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=16