On the climate prediction of nonlinear and non-stationary time series with the EMD method
Wan Shi-Quan (万仕全)a, Feng Guo-Lin (封国林)bd, Dong Wen-Jie (董文杰)c, Li Jian-Ping (李建平)d, Gao Xin-Quan (高新全)e, He Wen-Ping (何文平)b
a Department of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China; b Department of Physics, Yangzhou University, Yangzhou 225009, China; c National Climate Centre of China, Beijing 100081, China; d Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; e Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 10College of Atmospheric Science, Lanzhou University, Lanzhou 730000, China
Abstract At present, most of the statistical prediction models are built on the basis of the hypothesis that the time series or the observation data are linear and stationary. However, the observations are ordinarily nonlinear and non-stationary in nature, which are very difficult to be predicted by those models. Aiming at the nonlinearity/non-stationarity of the observation data, we introduce a new prediction scheme in this paper, in which firstly using the empirical mode decomposition the observations are stationarized and a variety of intrinsic mode functions (IMF) are obtained; secondly the IMFs are predicted by the mean generating function model separately; finally the predictions are used as new samples to fit and predict the original series. Research results show that the individual IMF, especially the eigen-IMF (namely eigen-hierarchy), has more stable predictability than the traditional methods. The scheme may effectively provide a new approach for the climate prediction.
Received: 13 September 2004
Revised: 26 November 2004
Accepted manuscript online:
(Probability theory, stochastic processes, and statistics)
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos 40275031 and 40325015), the National Key Programme Development for Basic Research (Grant No 2004CB418300) and Jiangsu Province Key Laboratory of Meteorological Disaster and
Cite this article:
Wan Shi-Quan (万仕全), Feng Guo-Lin (封国林), Dong Wen-Jie (董文杰), Li Jian-Ping (李建平), Gao Xin-Quan (高新全), He Wen-Ping (何文平) On the climate prediction of nonlinear and non-stationary time series with the EMD method 2005 Chinese Physics 14 628
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