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| Titel |
How to analyse irregularly sampled geophysical time series? |
| VerfasserIn |
Deniz Eroglu, Ibrahim Ozken, Thomas Stemler, Norbert Marwan, Karl-Heinz Wyrwoll, Juergen Kurths |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250105000
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| Publikation (Nr.) |
 EGU/EGU2015-4445.pdf |
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| Zusammenfassung |
| One of the challenges of time series analysis is to detect dynamical
changes in the dynamics of the underlying system.There are numerous
methods that can be used to detect such regime changes in regular
sampled times series. Here we present a new approach, that can be
applied, when the time series is irregular sampled. Such data sets occur
frequently in real world applications as in paleo climate proxy records.
The basic idea follows Victor and Purpura [1] and considers
segments of the time series. For each segment we compute the cost of
transforming the segment into the following one. If the time series is
from one dynamical regime the cost of transformation should be similar
for each segment of the data. Dramatic changes in the cost time series
indicate a change in the underlying dynamics. Any kind of analysis can
be applicable to the cost time series since it is a regularly sampled
time series. While recurrence plots are not the best choice for
irregular sampled data with some measurement noise component, we show
that a recurrence plot analysis based on the cost time series can
successfully identify the changes in the dynamics of the system.
We tested this method using synthetically created time series and will
use these results to highlight the performance of our method.
Furthermore we present our analysis of a suite of calcite and aragonite
stalagmites located in the eastern Kimberley region of tropical Western
Australia. This oxygen isotopic data is a proxy for the monsoon activity
over the last 8,000 years. In this time series our method picks up
several so far undetected changes from wet to dry in the monsoon system
and therefore enables us to get a better understanding of the monsoon
dynamics in the North-East of Australia over the last couple of thousand
years.
[1] J. D. Victor and K. P. Purpura, Network: Computation in Neural Systems 8, 127 (1997) |
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