Identifying the Most Relevant Lag with Runs

Faura, Úrsula; Lafuente, Matilde; Matilla-García, Mariano; Ruiz, Manuel

doi:10.3390/e17052706

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…As shown here, the ACF fails to identify the presence of a time-delay in NLMA time series. In line with the arguments put forward by Faura et al [13], we argue that the nonlinearity in the model is the main factor that reduces the efficacy of the ACF.…”

Section: Moving Average Modelssupporting

confidence: 89%

“…Moreover, several heterogeneous scientific fields could benefit from advances along this research line. Even though several methods have been introduced for time-delay estimation from time series [12] (and references therein) [10,13], the general question about which is the optimal approach or strategy remains open. Trying to give a step forward in this direction, in this paper, we analyze the ability of three quantifiers based on ordinal patterns to unveil delay dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Time-Delay Identification Using Multiscale Ordinal Quantifiers

Soriano

Zunino

2021

Entropy

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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.

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Section: Moving Average Modelssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Time-Delay Identification Using Multiscale Ordinal Quantifiers

Soriano

Zunino

2021

Entropy

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Statistical Tests of Symbolic Dynamics

et al. 2021

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A novel general method for constructing nonparametric hypotheses tests based on the field of symbolic analysis is introduced in this paper. Several existing tests based on symbolic entropy that have been used for testing central hypotheses in several branches of science (particularly in economics and statistics) are particular cases of this general approach. This family of symbolic tests uses few assumptions, which increases the general applicability of any symbolic-based test. Additionally, as a theoretical application of this method, we construct and put forward four new statistics to test for the null hypothesis of spatiotemporal independence. There are very few tests in the specialized literature in this regard. The new tests were evaluated with the mean of several Monte Carlo experiments. The results highlight the outstanding performance of the proposed test.

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Identifying the Most Relevant Lag with Runs

Cited by 2 publications

References 12 publications

Time-Delay Identification Using Multiscale Ordinal Quantifiers

Time-Delay Identification Using Multiscale Ordinal Quantifiers

Statistical Tests of Symbolic Dynamics

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