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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 |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 1, no. 2 ; Nr. 1, no. 2 (2014-12-21), S.1947-1966 |
| Datensatznummer |
250115140
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| Publikation (Nr.) |
 copernicus.org/npgd-1-1947-2014.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
will develop an improved SSA (ISSA) for processing the incomplete
time series and the modified SSA (SSAM) of Schoellhamer (2001) is
its special case. The approach was 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. Besides, both the
mean absolute errors and mean root mean squared errors of the
reconstructed time series by ISSA are also much 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 SD derived by ISSA is
12.27 mg L−1, smaller than 13.48 mg L−1 derived by SSAM. |
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