Nonstationary Time Series Prediction by Incorporating External Forces

WANG Geli; YANG Peicai; ZHOU Xiuji

doi:10.1007/s00376-013-2134-z

Abstract:

Almost all climate time series have some degree of nonstationarity due to external forces of the observed system. Therefore, these external forces should be taken into account when reconstructing the climate dynamics. This paper presents a novel technique in predicting nonstationary time series. The main difference of this new technique from some previous methods is that it incorporates the driving forces in the prediction model. To appraise its effectiveness, three prediction experiments were carried out using the data generated from some known classical dynamical models and a climate model with multiple external forces. Experimental results indicate that this technique is able to improve the prediction skill effectively.

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Nonstationary Time Series Prediction by Incorporating External Forces

Corresponding author: WANG Geli;

1. Key Laboratory of Middle Atmosphere and Global Environment Observations, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;

2. Chinese Academy of Meteorological Sciences, Beijing 100081

Manuscript received: 2012-07-21

Manuscript revised: 2012-10-24

Fund Project: This research was supported by the National Natural Science Foundation of China under Grant Nos. 40890052, 41075061, and 41275087. The authors are grateful to the anonymous referees and editors for their valuable suggestions.

Abstract: Almost all climate time series have some degree of nonstationarity due to external forces of the observed system. Therefore, these external forces should be taken into account when reconstructing the climate dynamics. This paper presents a novel technique in predicting nonstationary time series. The main difference of this new technique from some previous methods is that it incorporates the driving forces in the prediction model. To appraise its effectiveness, three prediction experiments were carried out using the data generated from some known classical dynamical models and a climate model with multiple external forces. Experimental results indicate that this technique is able to improve the prediction skill effectively.

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Nonstationary Time Series Prediction by Incorporating External Forces

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