Quantifying Deviations from Gaussianity with Application to Flight Delay Distributions

We propose a novel approach for quantifying deviations from Gaussianity by leveraging the Jensen-Shannon distance. Using stable distributions as a flexible framework, we analyze the effects of skewness and heavy tails in synthetic sequences. We employ phase-randomized surrogates as Gaussian referenc...

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Detalles Bibliográficos
Autores: Olivares, Felipe, Zanin, Massimiliano
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/401584
Acceso en línea:http://hdl.handle.net/10261/401584
http://arxiv.org/abs/2503.05834v1
Access Level:acceso abierto
Palabra clave:Jensen–Shannon divergence
Air traffic management
Flight delays
Non-Gaussian distributions
Ordinal patterns
Stable distributions
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spelling Quantifying Deviations from Gaussianity with Application to Flight Delay DistributionsOlivares, FelipeZanin, MassimilianoJensen–Shannon divergenceAir traffic managementFlight delaysNon-Gaussian distributionsOrdinal patternsStable distributionsWe propose a novel approach for quantifying deviations from Gaussianity by leveraging the Jensen-Shannon distance. Using stable distributions as a flexible framework, we analyze the effects of skewness and heavy tails in synthetic sequences. We employ phase-randomized surrogates as Gaussian references to systematically evaluate the statistical distance between this reference and stable distributions. Our methodology is validated using real flight delay datasets from major airports in Europe and the United States, revealing significant deviations from Gaussianity, particularly at high-traffic airports. These results highlight systematic air traffic management strategy differences between the two geographic regions.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No. 851255). This work was partially supported by the María de Maeztu project CEX2021-001164-M funded by the MICIU/AEI/10.13039/501100011033.With funding from the Spanish government through the "María de Maeztu Unit of Excellence" accreditation (CEX2021-001164-M)Peer reviewedMultidisciplinary Digital Publishing InstituteEuropean Research CouncilEuropean CommissionMinisterio de Ciencia e Innovación (España)Agencia Estatal de Investigación (España)Ministerio de Ciencia, Innovación y Universidades (España)Olivares, Felipe [0000-0002-6212-8865]Zanin, Massimiliano [0000-0002-5839-0393]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/401584http://arxiv.org/abs/2503.05834v1reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/851255info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2021-001164-MThe underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.3390/e27040354https://doi.org/10.3390/e27040354Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4015842026-05-22T06:33:51Z
dc.title.none.fl_str_mv Quantifying Deviations from Gaussianity with Application to Flight Delay Distributions
title Quantifying Deviations from Gaussianity with Application to Flight Delay Distributions
spellingShingle Quantifying Deviations from Gaussianity with Application to Flight Delay Distributions
Olivares, Felipe
Jensen–Shannon divergence
Air traffic management
Flight delays
Non-Gaussian distributions
Ordinal patterns
Stable distributions
title_short Quantifying Deviations from Gaussianity with Application to Flight Delay Distributions
title_full Quantifying Deviations from Gaussianity with Application to Flight Delay Distributions
title_fullStr Quantifying Deviations from Gaussianity with Application to Flight Delay Distributions
title_full_unstemmed Quantifying Deviations from Gaussianity with Application to Flight Delay Distributions
title_sort Quantifying Deviations from Gaussianity with Application to Flight Delay Distributions
dc.creator.none.fl_str_mv Olivares, Felipe
Zanin, Massimiliano
author Olivares, Felipe
author_facet Olivares, Felipe
Zanin, Massimiliano
author_role author
author2 Zanin, Massimiliano
author2_role author
dc.contributor.none.fl_str_mv European Research Council
European Commission
Ministerio de Ciencia e Innovación (España)
Agencia Estatal de Investigación (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Olivares, Felipe [0000-0002-6212-8865]
Zanin, Massimiliano [0000-0002-5839-0393]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Jensen–Shannon divergence
Air traffic management
Flight delays
Non-Gaussian distributions
Ordinal patterns
Stable distributions
topic Jensen–Shannon divergence
Air traffic management
Flight delays
Non-Gaussian distributions
Ordinal patterns
Stable distributions
description We propose a novel approach for quantifying deviations from Gaussianity by leveraging the Jensen-Shannon distance. Using stable distributions as a flexible framework, we analyze the effects of skewness and heavy tails in synthetic sequences. We employ phase-randomized surrogates as Gaussian references to systematically evaluate the statistical distance between this reference and stable distributions. Our methodology is validated using real flight delay datasets from major airports in Europe and the United States, revealing significant deviations from Gaussianity, particularly at high-traffic airports. These results highlight systematic air traffic management strategy differences between the two geographic regions.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/401584
http://arxiv.org/abs/2503.05834v1
url http://hdl.handle.net/10261/401584
http://arxiv.org/abs/2503.05834v1
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/H2020/851255
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2021-001164-M
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.3390/e27040354
https://doi.org/10.3390/e27040354
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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