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Kim, Pilwon
Nonlinear and Complex Dynamics
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dc.citation.endPage 20327 -
dc.citation.number 11 -
dc.citation.startPage 20313 -
dc.citation.title AIMS MATHEMATICS -
dc.citation.volume 7 - Choi, Jaesung - Kim, Pilwon - 2023-12-21T13:41:36Z - 2023-12-21T13:41:36Z - 2022-09-18 - 2022-09 -
dc.description.abstract Detecting critical transitions before they occur is challenging, especially for complex dynamical systems. While some early-warning indicators have been suggested to capture the phenomenon of slowing down in the system's response near critical transitions, their applicability to real systems is yet limited. In this paper, we propose the concept of predictability based on machine learning methods, which leads to an alternative early-warning indicator. The predictability metric takes a black-box approach and assesses the impact of uncertainties itself in identifying abrupt transitions in time series. We have applied the proposed metric to the time series generated from different systems, including an ecological model and an electric power system. We show that the predictability changes noticeably before critical transitions occur, while other general indicators such as variance and autocorrelation fail to make any notable signals. -
dc.identifier.bibliographicCitation AIMS MATHEMATICS, v.7, no.11, pp.20313 - 20327 -
dc.identifier.doi 10.3934/math.20221112 -
dc.identifier.issn 2473-6988 -
dc.identifier.scopusid 2-s2.0-85137990528 -
dc.identifier.uri -
dc.identifier.wosid 000860313800007 -
dc.language 영어 -
dc.publisher PO BOX 2604, SPRINGFIELD, USA, MO, 65801-2604 -
dc.title Early warning for critical transitions using machine-based predictability -
dc.type Article -
dc.description.isOpenAccess TRUE -
dc.relation.journalWebOfScienceCategory Mathematics, Applied;Mathematics -
dc.relation.journalResearchArea Mathematics -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor early-warning indicator -
dc.subject.keywordAuthor time series -
dc.subject.keywordAuthor predictability -
dc.subject.keywordAuthor critical transitions -
dc.subject.keywordPlus VOLTAGE COLLAPSE -
dc.subject.keywordPlus SYSTEMS -


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