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| A Recent Approach Incorporating External Forces to Predict Nonstationary Processes |
| WANG Ge-Li,YANG Pei-Cai |
| Key Laboratory of Middle Atmosphere and Global Environment Observations (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China,Key Laboratory of Middle Atmosphere and Global Environment Observations (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China |
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Abstract Most real-world time series have some degree of nonstationarity due to external perturbations of the observed system; external driving forces are the essential reason that leads to the nonstationarity of dynamics system. In this paper, the authors present a novel technique in which the authors incorporate external forces to predict nonstationary time series. To test the effect, the authors also examined two prediction experiments with an ideal time series from a logistic map and a proxy climate dataset for the past millennium. The preliminary results show that the resulting algorithm has better predictive ability than the one that does not consider the external forces.
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Received: 27 February 2010
Revised: 05 May 2010
Accepted: 05 May 2010
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