@article{dec37bf84b1141c48faf70ef4d0e8c2e,
title = "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 (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.",
author = "Uri Hasson and Jacopo Iacovacci and Ben Davis and Ryan Flanagan and Enzo Tagliazucchi and Helmut Laufs and Lucas Lacasa",
note = "Funding Information: We wish to thank Dr. Alon Angert for his advice on meteorological time series. UH{\textquoteright}s work was conducted in part while serving at and with support of the National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF. LL{\textquoteright}s acknowledges funding from an EPSRC Early Career Fellowship EP/P01660X/1. Funding Information: We wish to thank Dr. Alon Angert for his advice on meteorological time series. UH's work was conducted in part while serving at and with support of the National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF. LL's acknowledges funding from an EPSRC Early Career Fellowship EP/P01660X/1. Publisher Copyright: {\textcopyright} 2018 The Author(s).",
year = "2018",
month = dec,
day = "1",
doi = "10.1038/s41598-018-21785-0",
language = "English (US)",
volume = "8",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",
}