TY  - JOUR
A1  - Hasson, Uri
A1  - Iacovacci, Jacopo
A1  - Davis, Ben
A1  - Flanagan, Ryan
A1  - Tagliazucchi, Enzo
A1  - Laufs, Helmut
A1  - Lacasa, Lucas
T1  - A combinatorial framework to quantify peak/pit asymmetries in complex dynamics
T2  - Scientific reports
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.
KW  - Cognitive neuroscience
KW  - Complex networks
Y1  - 2018
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/45824
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-458249
SN  - 2045-2322
N1  - Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
VL  - 8
IS  - 1, Art. 3557
SP  - 1
EP  - 17
PB  - Macmillan Publishers Limited, part of Springer Nature
CY  - [London]
ER  -