Nonstationary time series prediction combined with slow feature analysis

Wang, G.; Chen, X.

doi:https://doi.org/10.5194/npg-22-377-2015

Articles | Volume 22, issue 4

https://doi.org/10.5194/npg-22-377-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/npg-22-377-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

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

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Final revised paper (published on 10 Jul 2015)
Preprint (discussion started on 26 Jan 2015)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC C38: 'npg-2014-82', Anonymous Referee #1, 22 Feb 2015
- AC C43: ''Reply to referee #1', by G.Wang', Geli Wang, 01 Mar 2015
RC C40: 'Referee Comment', Anonymous Referee #2, 23 Feb 2015
- AC C44: 'Reply to Referee #2', Geli Wang, 02 Mar 2015
RC C42: 'Referee comment', Anonymous Referee #3, 24 Feb 2015
- AC C46: 'Reply to Referee #3', Geli Wang, 02 Mar 2015

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

AR by Geli Wang on behalf of the Authors (17 Jun 2015)

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.