Transformation-cost time-series method for analyzing irregularly sampled data

Ibrahim Ozken*, Deniz Eroglu, Thomas Stemler, Norbert Marwan, G. Baris Bagci, Juergen Kurths

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Citations (Scopus)
4 Downloads (Pure)

Abstract

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations-with associated costs-to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rossler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

Original languageEnglish
Article number062911
Number of pages8
JournalPhysical Review. E, Statistical, Nonlinear and Soft Matter Physics
Volume91
Issue number6
DOIs
Publication statusPublished - 18 Jun 2015

Keywords

  • Last glacial period
  • recurrence plots
  • systems

Cite this

Transformation-cost time-series method for analyzing irregularly sampled data. / Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G. Baris; Kurths, Juergen.

In: Physical Review. E, Statistical, Nonlinear and Soft Matter Physics, Vol. 91, No. 6, 062911, 18.06.2015.

Research output: Contribution to journalArticle

Ozken, Ibrahim ; Eroglu, Deniz ; Stemler, Thomas ; Marwan, Norbert ; Bagci, G. Baris ; Kurths, Juergen. / Transformation-cost time-series method for analyzing irregularly sampled data. In: Physical Review. E, Statistical, Nonlinear and Soft Matter Physics. 2015 ; Vol. 91, No. 6.
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N1 - ACKNOWLEDGEMENTS We thank K.-H. Wyrwoll and F. McRobie from the School of Earth and Environment (UWA) for fruitful discussions. Moreover, we acknowledge for financial supports from TUBITAK under the 2214/A program (I.O.), from the Leibniz Association (WGL) under Grant No. SAW-2013-IZW-2542 (D.E.), as well as from the BMBF within the Potsdam Research Cluster for Georisk Analysis, Environmental Change and Sustainability (PROGRESS) Support Code No. 03IS2191B (N.M.).

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N2 - Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations-with associated costs-to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rossler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

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