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dc.contributor.author
Soriano, Miguel C.  
dc.contributor.author
Zunino, Luciano José  
dc.date.available
2022-10-18T01:50:29Z  
dc.date.issued
2021-08  
dc.identifier.citation
Soriano, Miguel C.; Zunino, Luciano José; Time-delay identification using multiscale ordinal quantifiers; Molecular Diversity Preservation International; Entropy; 23; 8; 8-2021; 1-15  
dc.identifier.issn
1099-4300  
dc.identifier.uri
http://hdl.handle.net/11336/173643  
dc.description.abstract
Time-delayed interactions naturally appear in a multitude of real-world systems due to the finite propagation speed of physical quantities. Often, the time scales of the interactions are unknown to an external observer and need to be inferred from time series of observed data. We explore, in this work, the properties of several ordinal-based quantifiers for the identification of time-delays from time series. To that end, we generate artificial time series of stochastic and deterministic time-delay models. We find that the presence of a nonlinearity in the generating model has consequences for the distribution of ordinal patterns and, consequently, on the delay-identification qualities of the quantifiers. Here, we put forward a novel ordinal-based quantifier that is particularly sensitive to nonlinearities in the generating model and compare it with previously-defined quantifiers. We conclude from our analysis on artificially generated data that the proper identification of the presence of a time-delay and its precise value from time series benefits from the complementary use of ordinal-based quantifiers and the standard autocorrelation function. We further validate these tools with a practical example on real-world data originating from the North Atlantic Oscillation weather phenomenon.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Molecular Diversity Preservation International  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
AUTOCORRELATION FUNCTION  
dc.subject
LINEAR MODELS  
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NONLINEAR MODELS  
dc.subject
ORDINAL PATTERNS  
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ORDINAL TEMPORAL ASYMMETRY  
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PERMUTATION ENTROPY  
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SYMBOLIC ANALYSIS  
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TIME SERIES  
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TIME-DELAY  
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WEIGHTED PERMUTATION ENTROPY  
dc.subject.classification
Otras Ciencias Físicas  
dc.subject.classification
Ciencias Físicas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Time-delay identification using multiscale ordinal quantifiers  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2022-09-21T23:26:35Z  
dc.journal.volume
23  
dc.journal.number
8  
dc.journal.pagination
1-15  
dc.journal.pais
Suiza  
dc.journal.ciudad
Basel  
dc.description.fil
Fil: Soriano, Miguel C.. Consejo Superior de Investigaciones Científicas. Instituto de Física Interdisciplinar y Sistemas Complejos; España  
dc.description.fil
Fil: Zunino, Luciano José. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina  
dc.journal.title
Entropy  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3390/e23080969  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1099-4300/23/8/969