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| Titel |
Comparison of correlation analysis techniques for irregularly sampled time series |
| VerfasserIn |
K. Rehfeld, N. Marwan, J. Heitzig, J. Kurths |
| 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 ; 18, no. 3 ; Nr. 18, no. 3 (2011-06-23), S.389-404 |
| Datensatznummer |
250013925
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| Publikation (Nr.) |
 copernicus.org/npg-18-389-2011.pdf |
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| Zusammenfassung |
Geoscientific measurements often provide time series with irregular time
sampling, requiring either data reconstruction (interpolation) or
sophisticated methods to handle irregular sampling. We compare the linear
interpolation technique and different approaches for analyzing the
correlation functions and persistence of irregularly sampled time series, as
Lomb-Scargle Fourier transformation and kernel-based methods. In a thorough
benchmark test we investigate the performance of these techniques.
All methods have comparable root mean square errors (RMSEs) for low skewness
of the inter-observation time distribution. For high skewness, very irregular
data, interpolation bias and RMSE increase strongly. We find a 40 % lower
RMSE for the lag-1 autocorrelation function (ACF) for the Gaussian kernel
method vs. the linear interpolation scheme,in the analysis of highly
irregular time series. For the cross correlation function (CCF) the RMSE is
then lower by 60 %. The application of the Lomb-Scargle technique gave
results comparable to the kernel methods for the univariate, but poorer
results in the bivariate case. Especially the high-frequency components of
the signal, where classical methods show a strong bias in ACF and CCF
magnitude, are preserved when using the kernel methods.
We illustrate the performances of interpolation vs. Gaussian kernel method by applying
both to paleo-data from four locations, reflecting late Holocene Asian
monsoon variability as derived from speleothem δ18O measurements.
Cross correlation results are similar for both methods, which we attribute to
the long time scales of the common variability. The persistence time (memory)
is strongly overestimated when using the standard, interpolation-based,
approach. Hence, the Gaussian kernel is a reliable and more robust estimator
with significant advantages compared to other techniques and suitable for
large scale application to paleo-data. |
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