Articles | Volume 22, issue 4
Research article
10 Jul 2015
Research article |  | 10 Jul 2015

Nonstationary time series prediction combined with slow feature analysis

G. Wang and X. Chen


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Geli Wang on behalf of the Authors (03 Apr 2015)  Author's response    Manuscript
ED: Publish as is (16 Jun 2015) by Valerio Lucarini
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.