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

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.