| 经验模态分解法在大气时间序列预测中的应用 |
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| 引用本文： | 玄兆燕,杨公训.经验模态分解法在大气时间序列预测中的应用[J].自动化学报,2008,34(1):97-101. |
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| 作者姓名： | 玄兆燕 杨公训 |
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| 作者单位： | 1.中国矿业大学 北京 100080 |
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| 摘 要： | 介绍了一种可以提高非平稳时间序列预测精度的新方法, 该方法应用 Hilbert-Huang 变换的核心内容---经验模态分解法 (Empirical mode decomposition, EMD) 对非平稳时间序列进行分解, 以降低被预测信号中的非平稳性, 利用神经网络对分解后的各分量进行预测, 再将预测结果叠加. 利用该方法对石家庄市年逐月降水量进行预测, 预测结果显示, 其预测精度比直接用神经网络预测的预测精度有较明显的提高.
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| 关 键 词： | Hilbert-Huang变换 预测 非平稳性 非线性 经验模态分解法(EMD) 人工神经网络(ANN) 时间序列 |
| 收稿时间： | 2006-09-19 |
| 修稿时间： | 2007-02-06 |
Application of EMD in the Atmosphere Time Series Prediction |
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| XUAN Zhao-Yan,YANG Gong-Xun.Application of EMD in the Atmosphere Time Series Prediction[J].Acta Automatica Sinica,2008,34(1):97-101. |
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| Authors: | XUAN Zhao-Yan YANG Gong-Xun |
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| Affiliation: | 1.China University of Mining and Technology, Beijing 100080;2.Hebei Polytechnic University, Tangshan 063009 |
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| Abstract: | 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. |
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| Keywords: | Hilbert-Huang transform prediction nonstationary non-linear empirical mode decomposition(EMD) artificial neural network(ANN) time series |
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