中国物理B ›› 2005, Vol. 14 ›› Issue (3): 628-633.doi: 10.1088/1009-1963/14/3/036

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On the climate prediction of nonlinear and non-stationary time series with the EMD method

高新全1, 万仕全2, 何文平3, 封国林4, 李建平5, 董文杰6   

  1. (1)College of Atmospheric Science, Lanzhou University, Lanzhou 730000, China; (2)Department of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China; (3)Department of Physics, Yangzhou University, Yangzhou 225009, China; (4)Department of Physics, Yangzhou University, Yangzhou 225009, China; Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; (5)Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 10
  • 收稿日期:2004-09-13 修回日期:2004-11-26 出版日期:2005-03-02 发布日期:2005-03-02
  • 基金资助:
    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

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   

  1. 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
  • Received:2004-09-13 Revised:2004-11-26 Online:2005-03-02 Published:2005-03-02
  • Supported by:
    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

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摘要： 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.

关键词: empirical mode decomposition, nonlinear/non-stationary time series, hierarchy theory, climate prediction

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.

Key words: empirical mode decomposition, nonlinear/non-stationary time series, hierarchy theory, climate prediction

中图分类号:  (Climatology, climate change and variability)

  • 92.60.Ry
05.45.Tp (Time series analysis) 02.50.-r (Probability theory, stochastic processes, and statistics)