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

Viewed

Total article views: 2,482 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,342 986 154 2,482 150 156
  • HTML: 1,342
  • PDF: 986
  • XML: 154
  • Total: 2,482
  • BibTeX: 150
  • EndNote: 156
Views and downloads (calculated since 26 Jan 2015)
Cumulative views and downloads (calculated since 26 Jan 2015)

Cited

Saved (final revised paper)

Latest update: 02 Nov 2024
Download
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.