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Journal Article

Recurrence analysis of extreme event-like data


Banerjee,  Abhirup
Potsdam Institute for Climate Impact Research;


Goswami,  Bedartha
Potsdam Institute for Climate Impact Research;

Hirata,  Yoshito
External Organizations;

Eroglu,  Deniz
External Organizations;

Merz,  Bruno
External Organizations;


Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;


Marwan,  Norbert
Potsdam Institute for Climate Impact Research;

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Banerjee, A., Goswami, B., Hirata, Y., Eroglu, D., Merz, B., Kurths, J., Marwan, N. (2021): Recurrence analysis of extreme event-like data. - Nonlinear Processes in Geophysics, 28, 2, 213-229.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_25458
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.