Time-delay identification using multiscale ordinal quantifiers

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 wo...

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Published in:Entropy
Main Authors: Soriano, Miguel C., Zunino, Luciano José
Format: Article in Journal/Newspaper
Language:English
Published: Molecular Diversity Preservation International
Subjects:
Online Access:http://hdl.handle.net/11336/173643
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author Soriano, Miguel C.
Zunino, Luciano José
author_facet Soriano, Miguel C.
Zunino, Luciano José
author_sort Soriano, Miguel C.
collection CONICET Digital (Consejo Nacional de Investigaciones Científicas y Técnicas)
container_issue 8
container_start_page 969
container_title Entropy
container_volume 23
description 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. Fil: Soriano, Miguel C. Consejo Superior de Investigaciones Científicas. Instituto de Física Interdisciplinar y Sistemas Complejos; España 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
format Article in Journal/Newspaper
genre North Atlantic
North Atlantic oscillation
genre_facet North Atlantic
North Atlantic oscillation
geographic Argentina
Zunino
geographic_facet Argentina
Zunino
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op_doi https://doi.org/10.3390/e23080969
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http://hdl.handle.net/11336/173643
Soriano, Miguel C.; Zunino, Luciano José; Time-delay identification using multiscale ordinal quantifiers; Molecular Diversity Preservation International; Entropy; 23; 8; 8-2021; 1-15
1099-4300
CONICET Digital
CONICET
op_rights info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
publisher Molecular Diversity Preservation International
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spelling ftconicet:oai:ri.conicet.gov.ar:11336/173643 2025-01-16T23:43:06+00:00 Time-delay identification using multiscale ordinal quantifiers Soriano, Miguel C. Zunino, Luciano José application/pdf http://hdl.handle.net/11336/173643 eng eng Molecular Diversity Preservation International info:eu-repo/semantics/altIdentifier/doi/10.3390/e23080969 info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1099-4300/23/8/969 http://hdl.handle.net/11336/173643 Soriano, Miguel C.; Zunino, Luciano José; Time-delay identification using multiscale ordinal quantifiers; Molecular Diversity Preservation International; Entropy; 23; 8; 8-2021; 1-15 1099-4300 CONICET Digital CONICET info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ AUTOCORRELATION FUNCTION LINEAR MODELS NONLINEAR MODELS ORDINAL PATTERNS ORDINAL TEMPORAL ASYMMETRY PERMUTATION ENTROPY SYMBOLIC ANALYSIS TIME SERIES TIME-DELAY WEIGHTED PERMUTATION ENTROPY https://purl.org/becyt/ford/1.3 https://purl.org/becyt/ford/1 info:eu-repo/semantics/article info:ar-repo/semantics/artículo info:eu-repo/semantics/publishedVersion ftconicet https://doi.org/10.3390/e23080969 2023-09-24T20:00:02Z 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. Fil: Soriano, Miguel C. Consejo Superior de Investigaciones Científicas. Instituto de Física Interdisciplinar y Sistemas Complejos; España 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 Article in Journal/Newspaper North Atlantic North Atlantic oscillation CONICET Digital (Consejo Nacional de Investigaciones Científicas y Técnicas) Argentina Zunino ENVELOPE(41.750,41.750,63.967,63.967) Entropy 23 8 969
spellingShingle AUTOCORRELATION FUNCTION
LINEAR MODELS
NONLINEAR MODELS
ORDINAL PATTERNS
ORDINAL TEMPORAL ASYMMETRY
PERMUTATION ENTROPY
SYMBOLIC ANALYSIS
TIME SERIES
TIME-DELAY
WEIGHTED PERMUTATION ENTROPY
https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
Soriano, Miguel C.
Zunino, Luciano José
Time-delay identification using multiscale ordinal quantifiers
title Time-delay identification using multiscale ordinal quantifiers
title_full Time-delay identification using multiscale ordinal quantifiers
title_fullStr Time-delay identification using multiscale ordinal quantifiers
title_full_unstemmed Time-delay identification using multiscale ordinal quantifiers
title_short Time-delay identification using multiscale ordinal quantifiers
title_sort time-delay identification using multiscale ordinal quantifiers
topic AUTOCORRELATION FUNCTION
LINEAR MODELS
NONLINEAR MODELS
ORDINAL PATTERNS
ORDINAL TEMPORAL ASYMMETRY
PERMUTATION ENTROPY
SYMBOLIC ANALYSIS
TIME SERIES
TIME-DELAY
WEIGHTED PERMUTATION ENTROPY
https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
topic_facet AUTOCORRELATION FUNCTION
LINEAR MODELS
NONLINEAR MODELS
ORDINAL PATTERNS
ORDINAL TEMPORAL ASYMMETRY
PERMUTATION ENTROPY
SYMBOLIC ANALYSIS
TIME SERIES
TIME-DELAY
WEIGHTED PERMUTATION ENTROPY
https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
url http://hdl.handle.net/11336/173643