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dc.contributor.author
Hasson, Uri
dc.contributor.author
Iacovacci, Jacopo
dc.contributor.author
Davis, Ben
dc.contributor.author
Flanagan, Ryan
dc.contributor.author
Tagliazucchi, Enzo Rodolfo
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dc.contributor.author
Laufs, Helmut
dc.contributor.author
Lacasa, Lucas
dc.date.available
2020-03-02T19:34:35Z
dc.date.issued
2018-12
dc.identifier.citation
Hasson, Uri; Iacovacci, Jacopo; Davis, Ben; Flanagan, Ryan; Tagliazucchi, Enzo Rodolfo; et al.; A combinatorial framework to quantify peak/pit asymmetries in complex dynamics; Nature Publishing Group; Scientific Reports; 8; 1; 12-2018; 1-17
dc.identifier.issn
2045-2322
dc.identifier.uri
http://hdl.handle.net/11336/98655
dc.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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Nature Publishing Group
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
NEUROIMAGING
dc.subject
STOCHASTIC PROCESSES
dc.subject.classification
Otras Ciencias Físicas
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dc.subject.classification
Ciencias Físicas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.subject.classification
Biofísica
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dc.subject.classification
Ciencias Biológicas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
A combinatorial framework to quantify peak/pit asymmetries in complex dynamics
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-10-22T17:51:49Z
dc.journal.volume
8
dc.journal.number
1
dc.journal.pagination
1-17
dc.journal.pais
Reino Unido
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dc.description.fil
Fil: Hasson, Uri. University of Chicago; Estados Unidos. University of Trento; Italia
dc.description.fil
Fil: Iacovacci, Jacopo. The Francis Crick Institute; Reino Unido. Imperial College London; Reino Unido
dc.description.fil
Fil: Davis, Ben. University of Trento; Italia
dc.description.fil
Fil: Flanagan, Ryan. Queen Mary University of London; Reino Unido
dc.description.fil
Fil: Tagliazucchi, Enzo Rodolfo. Netherlands Institute for Neuroscience; Países Bajos
dc.description.fil
Fil: Laufs, Helmut. Goethe Universitat Frankfurt; Alemania. University Hospital Kiel; Alemania
dc.description.fil
Fil: Lacasa, Lucas. Queen Mary University of London; Reino Unido
dc.journal.title
Scientific Reports
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1038/s41598-018-21785-0
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-018-21785-0
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