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| Titel |
Improved singular spectrum analysis for time series with missing data |
| VerfasserIn |
Y. Shen, F. Peng, B. Li |
| 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.371-376 |
| Datensatznummer |
250120987
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| Publikation (Nr.) |
 copernicus.org/npg-22-371-2015.pdf |
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| Zusammenfassung |
| Singular spectrum analysis (SSA) is a powerful technique for
time series analysis. Based on the property that the original time series
can be reproduced from its principal components, this contribution develops
an improved SSA (ISSA) for processing the incomplete time series and the
modified SSA (SSAM) of Schoellhamer (2001) is its special case. The approach
is evaluated with the synthetic and real incomplete time series data of
suspended-sediment concentration from San Francisco Bay. The result from the
synthetic time series with missing data shows that the relative errors of
the principal components reconstructed by ISSA are much smaller than those
reconstructed by SSAM. Moreover, when the percentage of the missing data
over the whole time series reaches 60 %, the improvements of relative
errors are up to 19.64, 41.34, 23.27 and 50.30 % for the first four
principal components, respectively. Both the mean absolute error
and mean root mean squared error of the reconstructed time series by ISSA
are also smaller than those by SSAM. The respective improvements are 34.45
and 33.91 % when the missing data accounts for 60 %. The results from
real incomplete time series also show that the standard deviation (SD)
derived by ISSA is 12.27 mg L−1, smaller than the 13.48 mg L−1 derived
by SSAM. |
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