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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  31 Oct 2020

31 Oct 2020

Review status
This preprint is currently under review for the journal NPG.

Recurrence analysis of extreme event like data

Abhirup Banerjee1,2, Bedartha Goswami1,a, Yoshito Hirata3, Deniz Eroglu4, Bruno Merz2,5, Jürgen Kurths1,6, and Norbert Marwan1,7 Abhirup Banerjee et al.
  • 1Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14412 Potsdam, Germany
  • 2Institute of Environmental Science and Geography, University of Potsdam, 14476 Potsdam, Germany
  • 3Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
  • 4Department of Bioinformatics and Genetics, Kadir Has University, 34083 Istanbul, Turkey
  • 5Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany
  • 6Institute of Physics, Humboldt Universität zu Berlin, Germany
  • 7Institute of Geoscience, University of Potsdam, 14476 Potsdam, Germany
  • acurrently at: University of Tübingen, 72074 Tübingen, Germany

Abstract. The identification of recurrences at various time scales in extreme event-like time series is challenging because of the rare occurrence of events which are separated by large temporal gaps. Most of the existing time series analysis techniques cannot be used to analyse extreme event-like time series in its unaltered form. The study of the system dynamics by reconstruction of the phase space using the standard delay embedding method is not directly applicable to event-like time series as it assumes a Euclidean notion of distance between states in the phase space. The edit distance method is a novel approach that uses the point-process nature of events. We propose a modification of edit distance to analyze the dynamics of extreme event-like time series by incorporating a nonlinear function which takes into account the sparse distribution of extreme events and utilizes the physical significance of their temporal pattern. We apply the modified edit distance method to event-like data generated from point process as well as flood event series constructed from discharge data of the Mississippi River in USA, and compute their recurrence plots. From the recurrence analysis, we are able to quantify the deterministic properties of extreme event-like data. We also show that there is a significant serial dependency in the flood time series by using the random shuffle surrogate method.

Abhirup Banerjee et al.

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Abhirup Banerjee et al.

Abhirup Banerjee et al.


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