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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 4
Nonlin. Processes Geophys., 22, 377–382, 2015
https://doi.org/10.5194/npg-22-377-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 22, 377–382, 2015
https://doi.org/10.5194/npg-22-377-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 10 Jul 2015

Research article | 10 Jul 2015

Nonstationary time series prediction combined with slow feature analysis

G. Wang1 and X. Chen1,2 G. Wang and X. Chen
  • 1Key Laboratory of Middle Atmosphere and Global Environment Observations, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2Pingtan Meteorological Bureau of Fujian Province, Pingtan 350400, China

Abstract. Almost all climate time series have some degree of nonstationarity due to external driving forces perturbing the observed system. Therefore, these external driving forces should be taken into account when constructing the climate dynamics. This paper presents a new technique of obtaining the driving forces of a time series from the slow feature analysis (SFA) approach, and then introduces them into a predictive model to predict nonstationary time series. The basic theory of the technique is to consider the driving forces as state variables and to incorporate them into the predictive model. Experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted to test the model. The results showed improved prediction skills.

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This paper presents a new technique of combining the driving force of a time series obtained using the slow feature analysis (SFA) approach, then introducing the driving force into a predictive model to predict nonstationary time series. It could be considered to be a data-driven attempt to make progress in predicting nonstationary climatic time series and in better understanding the climate causality research from observed climate data.
This paper presents a new technique of combining the driving force of a time series obtained...
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