Multifractional Brownian motion characterization based on Hurst exponent estimation and statistical learning.
Chaos
; 32(8): 083148, 2022 Aug.
Article
em En
| MEDLINE
| ID: mdl-36049911
ABSTRACT
This paper proposes an approach for the estimation of a time-varying Hurst exponent to allow accurate identification of multifractional Brownian motion (MFBM). The contribution provides a prescription for how to deal with the MFBM measurement data to solve regression and classification problems. Theoretical studies are supplemented with computer simulations and real-world examples. Those prove that the procedure proposed in this paper outperforms the best-in-class algorithm.
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Modelos Teóricos
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article