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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 |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 22, no. 4 ; Nr. 22, no. 4 (2015-07-10), S.377-382 |
| Datensatznummer |
250120988
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| Publikation (Nr.) |
 copernicus.org/npg-22-377-2015.pdf |
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| Zusammenfassung |
| Almost all climate time series have some degree of nonstationarity due to
external driving forces perturbing the observed system. Therefore, these
external driving forces should be taken into account when constructing the
climate dynamics. This paper presents a new technique of obtaining the
driving forces of a time series from the slow feature analysis (SFA)
approach, and then introduces them into a predictive model to predict
nonstationary time series. The basic theory of the technique is to consider
the driving forces as state variables and to incorporate them into the
predictive model. Experiments using a modified logistic time series and
winter ozone data in Arosa, Switzerland, were conducted to test the model.
The results showed improved prediction skills. |
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