Articles | Volume 22, issue 4
https://doi.org/10.5194/npg-22-377-2015
https://doi.org/10.5194/npg-22-377-2015
Research article
 | 
10 Jul 2015
Research article |  | 10 Jul 2015

Nonstationary time series prediction combined with slow feature analysis

G. Wang and X. Chen

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Short summary
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.