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Permutation entropy: Influence of amplitude information on time series classification performance

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Permutation entropy: Influence of amplitude information on time series classification performance

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Cuesta Frau, D. (2019). Permutation entropy: Influence of amplitude information on time series classification performance. Mathematical Biosciences and Engineering. 16(6):6842-6857. https://doi.org/10.3934/mbe.2019342

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/159336

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Title: Permutation entropy: Influence of amplitude information on time series classification performance
Author: Cuesta Frau, David
UPV Unit: Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Issued date:
Abstract:
[EN] Permutation Entropy (PE) is a very popular complexity analysis tool for time series. Despite its simplicity, it is very robust and yields goods results in applications related to assessing the randomness of a sequence, ...[+]
Subjects: Permutation entropy , Amplitude aware permutation entropy , Fine-grained permutation entropy , Weighted permutation entropy , Sample entropy , Time series classification
Copyrigths: Reconocimiento (by)
Source:
Mathematical Biosciences and Engineering. (issn: 1547-1063 )
DOI: 10.3934/mbe.2019342
Publisher version: https://doi.org/10.3934/mbe.2019342
Type: Artículo

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