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  Prediction of flow dynamics using point processes

Hirata, Y., Stemler, T., Eroglu, D., Marwan, N. (2018): Prediction of flow dynamics using point processes. - Chaos, 28, 1, 011101.
https://doi.org/10.1063/1.5016219

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 Creators:
Hirata, Y.1, Author
Stemler, Thomas2, Author              
Eroglu, Deniz2, Author              
Marwan, Norbert2, Author              
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Describing a time series parsimoniously is the first step to study the underlying dynamics. For a time-discrete system, a generating partition provides a compact description such that a time series and a symbolic sequence are one-to-one. But, for a time-continuous system, such a compact description does not have a solid basis. Here, we propose to describe a time-continuous time series using a local cross section and the times when the orbit crosses the local cross section. We show that if such a series of crossing times and some past observations are given, we can predict the system's dynamics with fine accuracy. This reconstructability neither depends strongly on the size nor the placement of the local cross section if we have a sufficiently long database. We demonstrate the proposed method using the Lorenz model as well as the actual measurement of wind speed.

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 Dates: 2018
 Publication Status: Finally published
 Pages: -
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 Rev. Type: Peer
 Identifiers: DOI: 10.1063/1.5016219
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 8087
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
Organisational keyword: RD4 - Complexity Science
Working Group: Development of advanced time series analysis techniques
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Title: Chaos
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 28 (1) Sequence Number: 011101 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808