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| Titel |
Improved preservation of autocorrelative structure in surrogate data using an initial wavelet step |
| VerfasserIn |
C. J. Keylock |
| 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 ; 15, no. 3 ; Nr. 15, no. 3 (2008-06-02), S.435-444 |
| Datensatznummer |
250012659
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| Publikation (Nr.) |
 copernicus.org/npg-15-435-2008.pdf |
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| Zusammenfassung |
| Surrogate data generation algorithms are useful for hypothesis testing or
for generating realisations of a process for data extension or modelling
purposes. This paper tests a well known surrogate data generation method
against a stochastic and also a hybrid wavelet-Fourier transform variant of
the original algorithm. The data used for testing vary in their persistence
and intermittency, and include synthetic and actual data. The hybrid
wavelet-Fourier algorithm outperforms the others in its ability to match the
autocorrelation function of the data, although the advantages decrease for
high intermittencies and when attention is only directed towards the early
part of the autocorrelation function. The improved performance is attributed
to the wavelet step of the algorithm. |
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