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CITATION.cff
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cff-version: 0.1.3
title: knnpe
message: >-
knnp: A Python package for characterizing unstructured data with the
nearest neighbor permutation entropy
type: software
authors:
- given-names: Leonardo G. J. M.
family-names: Voltarelli
email: [email protected]
affiliation: >-
Departamento de Física, Universidade Estadual de
Maringá, Maringá, PR 87020-900, Brazil
orcid: 'https://orcid.org/0009-0006-7198-503X'
- given-names: Arthur A. B.
family-names: Pessa
email: [email protected]
affiliation: >-
Departamento de Física, Universidade Estadual de
Maringá, Maringá, PR 87020-900, Brazil
orcid: 'https://orcid.org/0000-0002-4942-0618'
- given-names: Luciano
family-names: Zunino
email: [email protected]
affiliation: >-
Departamento de Ciencias Básicas, Facultad de Ingeniería,
Universidad Nacional de La Plata (UNLP), 1900 La Plata, Argentina
orcid: 'https://orcid.org/0000-0002-2852-3263'
- given-names: Rafael S.
family-names: Zola
email: [email protected]
affiliation: >-
Departamento de Física, Universidade Tecnológica Federal do
Paraná, Apucarana, PR 86812-460, Brazil
orcid: 'https://orcid.org/0000-0003-4930-5262'
- given-names: Ervin K.
family-names: Lenzi
email: [email protected]
affiliation: >-
Departamento de Física, Universidade Estadual de Ponta Grossa,
Ponta Grossa, PR 84030-900, Brazil
orcid: 'https://orcid.org/0000-0003-3853-1790'
- given-names: Matjaz
family-names: Perc
email: [email protected]
affiliation: >-
Faculty of Natural Sciences and Mathematics,
University of Maribor, Koroška cesta 160,
2000 Maribor, Slovenia
orcid: 'https://orcid.org/0000-0002-3087-541X'
- given-names: Haroldo V.
family-names: Ribeiro
affiliation: >-
Departamento de Física, Universidade Estadual de
Maringá, Maringá, PR 87020-900, Brazil
email: [email protected]
orcid: 'https://orcid.org/0000-0002-9532-5195'
identifiers:
- type: doi
value: 10.1063/?
- type: other
value: 'https://arxiv.org/abs/?'
description: 'arXiv:'
repository-code: 'https://github.com/hvribeiro/knnpe'
url: 'https://arthurpessa.github.io/knnpe/'
abstract: >-
Permutation entropy and its associated frameworks are
remarkable examples of physics-inspired techniques
adept at processing complex and voluminous datasets.
Despite substantial progress in developing and
applying these tools, their use has been predominantly
limited to structured datasets such as time series or
images. Here, we introduce the k-nearest neighbor
permutation entropy, an innovative extension of the
permutation entropy tailored for unstructured data,
irrespective of their spatial or temporal configuration
and dimensionality. Our approach builds upon nearest
neighbor graphs to establish neighborhood relations
and uses random walks to extract ordinal patterns.
This tool not only adeptly identifies variations in
patterns of unstructured data, but also does so with
a precision that significantly surpasses conventional
measures such as spatial autocorrelation. Moreover,
the k-nearest neighbor permutation entropy provides
a natural approach for incorporating amplitude
information and time gaps when analyzing time series
or images, thus significantly enhancing its noise
resilience and predictive capabilities compared to the
usual permutation entropy. Our research substantially
expands the applicability of ordinal methods to broader
arrays of data types, opening promising research avenues
for extending the permutation entropy toolkit for
unstructured data.
license: MIT
preferred-citation:
type: article
authors:
- family-names: "Voltarelli"
given-names: "Leonardo G. J. M."
- family-names: "Pessa"
given-names: "Arthur A. B."
- family-names: "Zunino"
given-names: "Luciano"
- family-names: "Zola"
given-names: "Rafael S."
- family-names: "Lenzi"
given-names: "Ervin K."
- family-names: "Perc"
given-names: "Matjaz"
- family-names: "Ribeiro"
given-names: "Haroldo V."
doi: "10.1063/"
journal: "arXiv"
start: 0
title: "Characterizing unstructured data with the nearest neighbor permutation entropy"
issue: 0
volume: 0
year: 2024