LL’s acknowledges funding from an EPSRC Early Career Fellowship EP/P01660X/1

A Aragoneses

A Charakopoulos

A Mazaheri

AD Patel

AM Núñez

B Davis

B Fischl

B Luque

D Cordes

DN Greve

E Tagliazucchi

J Iacovacci

JF Donges

L Lacasa

M Ahmadlou

M Newman

MG Ravetti

N Petridou

R Flanagan

RM Birn

RV Donner

S Bhaduri

S Jiang

S Severini

SD Forman

TA Amor

V Suyal

X Liu

Y Zhang

Y Zou

Z-K Gao

ZG Shao

Scientific Reports

English

arXiv

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.Fil: Hasson, Uri. University of Chicago; Estados Unidos. University of Trento; ItaliaFil: Iacovacci, Jacopo. The Francis Crick Institute; Reino Unido. Imperial College London; Reino UnidoFil: Davis, Ben. University of Trento; ItaliaFil: Flanagan, Ryan. Queen Mary University of London; Reino UnidoFil: Tagliazucchi, Enzo Rodolfo. Netherlands Institute for Neuroscience; Países BajosFil: Laufs, Helmut. Goethe Universitat Frankfurt; Alemania. University Hospital Kiel; AlemaniaFil: Lacasa, Lucas. Queen Mary University of London; Reino Unid

Hasson, Uri

Iacovacci, Jacopo

Davis, Ben

Flanagan, Ryan

Tagliazucchi, Enzo Rodolfo

Laufs, Helmut

Lacasa, Lucas

CONICET Digital

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

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

Tagliazucchi, E.

NARCIS 

AbstractWe 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.</jats:p

Uri Hasson

Jacopo Iacovacci

Ben Davis

Ryan Flanagan

Enzo Tagliazucchi

Helmut Laufs

Lucas Lacasa

Crossref

Tagliazucchi, Enzo

Hochschulschriftenserver - Universität Frankfurt am Main

Hasson, U

Iacovacci, J

Davis, B

Flanagan, R

Tagliazucchi, E

Laufs, H

Lacasa, L

Queen Mary Research Online

https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/34124/1/Lacasa%20A%20combinatorial%20framework%202018%20Accepted.pdf

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

Abstract

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CONICET Digital

NARCIS

Crossref

Hochschulschriftenserver - Universität Frankfurt am Main

Queen Mary Research Online