A combinatorial framework to quantify peak/pit asymmetries in complex dynamics

Abstract We explore a combinatorial framework which efficiently quantifies the asymmetries between minima and maxima in local fluctuations of time series. We first showcase its performance by applying it to a battery of synthetic cases. We find rigorous results on some canonical dynamical models (st...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Uri Hasson, Jacopo Iacovacci, Ben Davis, Ryan Flanagan, Enzo Tagliazucchi, Helmut Laufs, Lucas Lacasa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c226ceca8caa459dbe2fc1442e72d860
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c226ceca8caa459dbe2fc1442e72d860
record_format	dspace
spelling	oai:doaj.org-article:c226ceca8caa459dbe2fc1442e72d8602021-12-02T11:40:46ZA combinatorial framework to quantify peak/pit asymmetries in complex dynamics10.1038/s41598-018-21785-02045-2322https://doaj.org/article/c226ceca8caa459dbe2fc1442e72d8602018-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-21785-0https://doaj.org/toc/2045-2322Abstract We explore a combinatorial framework which efficiently quantifies the asymmetries between minima and maxima in local fluctuations of time series. We first showcase its performance by applying it to a battery of synthetic cases. We find rigorous results on some canonical dynamical models (stochastic processes with and without correlations, chaotic processes) complemented by extensive numerical simulations for a range of processes which indicate that the methodology correctly distinguishes different complex dynamics and outperforms state of the art metrics in several cases. Subsequently, we apply this methodology to real-world problems emerging across several disciplines including cases in neurobiology, finance and climate science. We conclude that differences between the statistics of local maxima and local minima in time series are highly informative of the complex underlying dynamics and a graph-theoretic extraction procedure allows to use these features for statistical learning purposes.Uri HassonJacopo IacovacciBen DavisRyan FlanaganEnzo TagliazucchiHelmut LaufsLucas LacasaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-17 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Uri Hasson Jacopo Iacovacci Ben Davis Ryan Flanagan Enzo Tagliazucchi Helmut Laufs Lucas Lacasa A combinatorial framework to quantify peak/pit asymmetries in complex dynamics
description	Abstract We explore a combinatorial framework which efficiently quantifies the asymmetries between minima and maxima in local fluctuations of time series. We first showcase its performance by applying it to a battery of synthetic cases. We find rigorous results on some canonical dynamical models (stochastic processes with and without correlations, chaotic processes) complemented by extensive numerical simulations for a range of processes which indicate that the methodology correctly distinguishes different complex dynamics and outperforms state of the art metrics in several cases. Subsequently, we apply this methodology to real-world problems emerging across several disciplines including cases in neurobiology, finance and climate science. We conclude that differences between the statistics of local maxima and local minima in time series are highly informative of the complex underlying dynamics and a graph-theoretic extraction procedure allows to use these features for statistical learning purposes.
format	article
author	Uri Hasson Jacopo Iacovacci Ben Davis Ryan Flanagan Enzo Tagliazucchi Helmut Laufs Lucas Lacasa
author_facet	Uri Hasson Jacopo Iacovacci Ben Davis Ryan Flanagan Enzo Tagliazucchi Helmut Laufs Lucas Lacasa
author_sort	Uri Hasson
title	A combinatorial framework to quantify peak/pit asymmetries in complex dynamics
title_short	A combinatorial framework to quantify peak/pit asymmetries in complex dynamics
title_full	A combinatorial framework to quantify peak/pit asymmetries in complex dynamics
title_fullStr	A combinatorial framework to quantify peak/pit asymmetries in complex dynamics
title_full_unstemmed	A combinatorial framework to quantify peak/pit asymmetries in complex dynamics
title_sort	combinatorial framework to quantify peak/pit asymmetries in complex dynamics
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/c226ceca8caa459dbe2fc1442e72d860
work_keys_str_mv	AT urihasson acombinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT jacopoiacovacci acombinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT bendavis acombinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT ryanflanagan acombinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT enzotagliazucchi acombinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT helmutlaufs acombinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT lucaslacasa acombinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT urihasson combinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT jacopoiacovacci combinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT bendavis combinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT ryanflanagan combinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT enzotagliazucchi combinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT helmutlaufs combinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics AT lucaslacasa combinatorialframeworktoquantifypeakpitasymmetriesincomplexdynamics
_version_	1718395543529455616

A combinatorial framework to quantify peak/pit asymmetries in complex dynamics

Ejemplares similares