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

TY - JOUR

T1 - A combinatorial framework to quantify peak/pit asymmetries in complex dynamics

AU - Hasson, Uri

AU - Iacovacci, Jacopo

AU - Davis, Ben

AU - Flanagan, Ryan

AU - Tagliazucchi, E.

AU - Laufs, Helmut

AU - Lacasa, Lucas

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Journal Article

U2 - 10.1038/s41598-018-21785-0

DO - 10.1038/s41598-018-21785-0

M3 - Article

C2 - 29476077

SN - 2045-2322

VL - 8

JO - Scientific Reports

JF - Scientific Reports

IS - 1

M1 - 3557

ER -