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  A novel framework for direct multistep prediction in complex systems

Wu, T., An, F., Gao, X., Zhong, W., Kurths, J. (2023): A novel framework for direct multistep prediction in complex systems. - Nonlinear Dynamics, 111, 9289-9304.
https://doi.org/10.1007/s11071-023-08360-7

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 Creators:
Wu, Tao1, Author              
An, Feng2, Author
Gao, Xiangyun2, Author
Zhong, Weiqiong2, Author
Kurths, Jürgen1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: Multistep prediction is an open challenge in many real-world systems for a long time. Despite the advantages of previous approaches, e.g., step-by-step iteration, they have some shortcomings, such as accumulated errors, high cost, and low interpretation. To this end, Gaussian process regression and delay embedding are used to create a combination framework, namely spatial–temporal mapping (STM). Delay embedding is employed to reconstruct an isomorphic dynamical structure with the original system through a single time series, which provides the fundamental architecture for multistep predictions (interpretation). Gaussian process regression is used to achieve predictions by identifying a mapping between the reconstructed dynamical structure and the original structure. This combination framework outputs multistep ahead predictions in a single step (low cost). We test the feasibility of STM for both model systems, including the 3-species ecology system, the Lorenz chaotic system, and the Rossler chaotic system, and several real-world systems, involving energy, finance, life science, and climate. STM framework outperforms traditional iterative approaches and has the potential for many other real-world systems.

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Language(s): eng - English
 Dates: 2023-03-042023-05
 Publication Status: Finally published
 Pages: 16
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1007/s11071-023-08360-7
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Tipping Elements
Model / method: Machine Learning
Model / method: Nonlinear Data Analysis
 Degree: -

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Title: Nonlinear Dynamics
Source Genre: Journal, SCI, Scopus
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Publ. Info: -
Pages: - Volume / Issue: 111 Sequence Number: - Start / End Page: 9289 - 9304 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/nonlinear-dynamics
Publisher: Springer