The Prediction of Non-stationary Climate Series Based on Empirical Mode Decomposition |
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Authors: | YANG Peicai WANG Geli BIAN Jianchun and ZHOU Xiuji |
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Institution: | Key Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Key Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Key Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Chinese Academy of Meteorological Sciences, Beijing 100081 |
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Abstract: | This paper proposes a new approach which we refer to as ``segregated
prediction" to predict climate time series which are nonstationary. This
approach is based on the empirical mode decomposition method (EMD), which
can decompose a time signal into a finite and usually small number of basic
oscillatory components. To test the capabilities of this approach, some
prediction experiments are carried out for several climate time series. The
experimental results show that this approach can decompose the
nonstationarity of the climate time series and segregate nonlinear
interactions between the different mode components, which thereby is able to
improve prediction accuracy of these original climate time series. |
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Keywords: | EMD nonstationarity nonlinear system climate prediction time series prediction |
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