Efficient computation of recurrence quantification analysis via microstates

Froguel, Lucas Belasque; Prado, Thiago de Lima; Corso, Gilberto; Lima, Gustavo Zampier dos Santos; Lopes, S. R.

doi:10.1016/j.amc.2022.127175

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2024

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Cited by 6 publications

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“…Other recurrence quantifiers can also be written as functions of RM. The results are in general approximations for the ones obtained using all points available in a time series, but in general both way to compute the quantifiers agree very well [9].…”

Section: The Methodsmentioning

confidence: 67%

How to Compute Suitable Vicinity Parameter and Sampling Time of Recurrence Analysis

Prado,

Machado,

Corso

et al. 2022

SSRN Journal

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Section: The Methodsmentioning

confidence: 67%

How to Compute Suitable Vicinity Parameter and Sampling Time of Recurrence Analysis

Prado,

Machado,

Corso

et al. 2022

SSRN Journal

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How to compute suitable vicinity parameter and sampling time of recurrence analysis

de Lima Prado,

Machado,

Corso

et al. 2023

Nonlinear Dyn

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Recurrence microstates for machine learning classification

Spezzatto,

Flauzino,

Corso

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Recurrence microstates are obtained from the cross recurrence of two sequences of values embedded in a time series, being the generalization of the concept of recurrence of a given state in phase space. The probability of occurrence of each microstate constitutes a recurrence quantifier. The set of probabilities of all microstates are capable of detecting even small changes in the data pattern. This creates an ideal tool for generating features in machine learning algorithms. Thanks to the sensitivity of the set of probabilities of occurrence of microstates, it can be used to feed a deep neural network, namely, a microstate multi-layer perceptron (MMLP) to classify parameters of chaotic systems. Additionally, we show that with more microstates, the accuracy of the MMLP increases, showing that the increasing size and number of microstates insert new and independent information into the analysis. We also explore potential applications of the proposed method when adapted to different contexts.

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Efficient computation of recurrence quantification analysis via microstates

Cited by 6 publications

References 22 publications

How to Compute Suitable Vicinity Parameter and Sampling Time of Recurrence Analysis

How to Compute Suitable Vicinity Parameter and Sampling Time of Recurrence Analysis

How to compute suitable vicinity parameter and sampling time of recurrence analysis

Recurrence microstates for machine learning classification

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