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| Titel |
Technical Note: Comparing the effectiveness of recent algorithms to fill and smooth incomplete and noisy time series |
| VerfasserIn |
J. P. Musial, M. M. Verstraete, N. Gobron |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 11, no. 15 ; Nr. 11, no. 15 (2011-08-04), S.7905-7923 |
| Datensatznummer |
250009982
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| Publikation (Nr.) |
 copernicus.org/acp-11-7905-2011.pdf |
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| Zusammenfassung |
| Geophysical time series often feature missing data or data acquired at
irregular times. Procedures are needed to either resample these series at
systematic time intervals or to generate reasonable estimates at specified
times in order to meet specific user requirements or to facilitate subsequent
analyses. Interpolation methods have long been used to address this problem,
taking into account the fact that available measurements also include errors
of measurement or uncertainties. This paper inspects some of the currently
used approaches to fill gaps and smooth time series (smoothing splines,
Singular Spectrum Analysis and Lomb-Scargle) by comparing their performance
in either reconstructing the original record or in minimizing the Mean
Absolute Error (MAE), Mean Bias Error (MBE), chi-squared test statistics and
autocorrelation of residuals between the underlying model and the available
data, using both artificially-generated series or well-known publicly
available records. Some methods make no assumption on the type of variability
in the data while others hypothesize the presence of at least some dominant
frequencies. It will be seen that each method exhibits advantages and
drawbacks, and that the choice of an approach largely depends on the
properties of the underlying time series and the objective of the research. |
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