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  Predicting spatio-temporal time series using dimension reduced local states

Isensee, J., Datseris, G., & Parlitz, U. (2019). Predicting spatio-temporal time series using dimension reduced local states. Journal of Nonlinear Science, (in press). doi:10.1007/s00332-019-09588-7.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-1996-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-1997-5
Genre: Journal Article

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 Creators:
Isensee, Jonas1, Author              
Datseris, George2, Author              
Parlitz, Ulrich1, Author              
Affiliations:
1Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063288              
2Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063286              

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Free keywords: Data driven modelling; Nearest neighbours prediction; Spatio-temporal chaos
 Abstract: We present a method for both cross-estimation and iterated time series prediction of spatio-temporal dynamics based on local modelling and dimension reduction techniques. Assuming homogeneity of the underlying dynamics, we construct delay coordinates of local states and then further reduce their dimensionality through Principle Component Analysis. The prediction uses nearest neighbour methods in the space of dimension reduced states to either cross-estimate or iteratively predict the future of a given frame. The effectiveness of this approach is shown for (noisy) data from a (cubic) Barkley model, the Bueno-Orovio-Cherry-Fenton model, and the Kuramoto-Sivashinsky model.

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Language(s): eng - English
 Dates: 2019-10-26
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1007/s00332-019-09588-7
 Degree: -

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Title: Journal of Nonlinear Science
Source Genre: Journal
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Pages: - Volume / Issue: - Sequence Number: (in press) Start / End Page: - Identifier: -