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.

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Author:Uri Hasson, Jacopo Iacovacci, Ben Davis, Ryan Flanagan, Enzo Tagliazucchi, Helmut Laufs, Lucas Lacasa
URN:urn:nbn:de:hebis:30:3-458249
DOI:https://doi.org/10.1038/s41598-018-21785-0
ISSN:2045-2322
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/29476077
Parent Title (English):Scientific reports
Publisher:Macmillan Publishers Limited, part of Springer Nature
Place of publication:[London]
Document Type:Article
Language:English
Year of Completion:2018
Date of first Publication:2018/02/23
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/03/08
Tag:Cognitive neuroscience; Complex networks
Volume:8
Issue:1, Art. 3557
Page Number:17
First Page:1
Last Page:17
Note:
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/.
HeBIS-PPN:43209640X
Institutes:Medizin / Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0