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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ftmdpi:oai:mdpi.com:/1099-4300/23/8/969/ 2023-08-20T04:08:24+02:00 Time-Delay Identification Using Multiscale Ordinal Quantifiers Miguel C. Soriano Luciano Zunino 2021-07-27 application/pdf https://doi.org/10.3390/e23080969 EN eng Multidisciplinary Digital Publishing Institute Complexity https://dx.doi.org/10.3390/e23080969 https://creativecommons.org/licenses/by/4.0/ Entropy; Volume 23; Issue 8; Pages: 969 time-delay time series symbolic analysis ordinal patterns permutation entropy weighted permutation entropy Ordinal Temporal Asymmetry autocorrelation function linear models nonlinear models Text 2021 ftmdpi https://doi.org/10.3390/e23080969 2023-08-01T02:17:37Z 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. Text North Atlantic North Atlantic oscillation MDPI Open Access Publishing Entropy 23 8 969 |
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MDPI Open Access Publishing |
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language |
English |
topic |
time-delay time series symbolic analysis ordinal patterns permutation entropy weighted permutation entropy Ordinal Temporal Asymmetry autocorrelation function linear models nonlinear models |
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time-delay time series symbolic analysis ordinal patterns permutation entropy weighted permutation entropy Ordinal Temporal Asymmetry autocorrelation function linear models nonlinear models Miguel C. Soriano Luciano Zunino Time-Delay Identification Using Multiscale Ordinal Quantifiers |
topic_facet |
time-delay time series symbolic analysis ordinal patterns permutation entropy weighted permutation entropy Ordinal Temporal Asymmetry autocorrelation function linear models nonlinear models |
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. |
format |
Text |
author |
Miguel C. Soriano Luciano Zunino |
author_facet |
Miguel C. Soriano Luciano Zunino |
author_sort |
Miguel C. Soriano |
title |
Time-Delay Identification Using Multiscale Ordinal Quantifiers |
title_short |
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_sort |
time-delay identification using multiscale ordinal quantifiers |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/e23080969 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Entropy; Volume 23; Issue 8; Pages: 969 |
op_relation |
Complexity https://dx.doi.org/10.3390/e23080969 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/e23080969 |
container_title |
Entropy |
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23 |
container_issue |
8 |
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969 |
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1774720639735693312 |