Artículo
A combinatorial framework to quantify peak/pit asymmetries in complex dynamics
Hasson, Uri; Iacovacci, Jacopo; Davis, Ben; Flanagan, Ryan; Tagliazucchi, Enzo Rodolfo
; Laufs, Helmut; Lacasa, Lucas
![Icon](/themes/CONICETDigital/images/conicet.png)
Fecha de publicación:
12/2018
Editorial:
Nature Publishing Group
Revista:
Scientific Reports
ISSN:
2045-2322
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
Palabras clave:
NEUROIMAGING
,
STOCHASTIC PROCESSES
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Articulos(IFIBA)
Articulos de INST.DE FISICA DE BUENOS AIRES
Articulos de INST.DE FISICA DE BUENOS AIRES
Citación
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
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