%0 Journal Article
%T Application of EMD in the Atmosphere Time Series Prediction<br>经验模态分解法在大气时间序列预测中的应用
%A XUAN Zhao-Yan
%A YANG Gong-Xun
%A <br>玄兆燕
%A 杨公训
%J 自动化学报
%D 2008
%I 
%X In this paper,a new method to improve non- stationary time series prediction accuracy is introduced.The non-stationary time series is decomposed by empirical mode de- composition(EMD)in Hilbert-Huang transform to reduce the non-stationarity in the signals.By using neural network,the component of decomposition is predicted,then the predicted re- suits are added.The author has predicted monthly precipitation data at Shijiazhuang with the method.The study shows that the prediction accuracy of the neural network based on EMD is higher than that of prediction method using the neural network.
%K Hilbert-Huang transform
%K prediction
%K nonstationary
%K non-linear
%K empirical mode decomposition(EMD)
%K artificial neural network(ANN)
%K time series<br>Hilbert-Huang变换
%K 预测
%K 非平稳性
%K 非线性
%K 经验模态分解法(EMD)
%K 人工神经网络(ANN)
%K 时间序列
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=0DAE70687B31DD74208EE826810394F0&yid=67289AFF6305E306&vid=339D79302DF62549&iid=CA4FD0336C81A37A&sid=C3BF5C58156BEDF0&eid=74011071555EB4E5&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=4