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| Titel |
Nonstationary time series prediction combined with slow feature analysis |
| VerfasserIn |
G. Wang, X. Chen |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 2, no. 1 ; Nr. 2, no. 1 (2015-01-26), S.97-114 |
| Datensatznummer |
250115144
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| Publikation (Nr.) |
 copernicus.org/npgd-2-97-2015.pdf |
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| Zusammenfassung |
| Almost all climate time series have some degree of nonstationarity
due to external driving forces perturbations of the observed
system. Therefore, these external driving forces should be taken
into account when reconstructing the climate dynamics. 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
non-stationary time series. In essence, the main idea of the
technique is to consider the driving forces as state variables and
incorporate them into the prediction model. To test the method,
experiments using a modified logistic time series and winter ozone
data in Arosa, Switzerland, were conducted. The results showed
improved and effective prediction skill. |
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