Advanced Analysis of Temporal Data Using Fisher-Shannon Information: Theoretical Development and Application in Geosciences

Détails

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Etat: Public
Version: Final published version
Licence: Non spécifiée
ID Serval
serval:BIB_7B8DB4AFFC2C
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Advanced Analysis of Temporal Data Using Fisher-Shannon Information: Theoretical Development and Application in Geosciences
Périodique
Frontiers in Earth Science
Auteur(s)
Guignard Fabian, Laib Mohamed, Amato Federico, Kanevski Mikhail
ISSN
2296-6463
Statut éditorial
Publié
Date de publication
14/07/2020
Volume
8
Langue
anglais
Résumé
Complex non-linear time series are ubiquitous in geosciences. Quantifying complexity and non-stationarity of these data is a challenging task, and advanced complexity-based exploratory tool are required for understanding and visualizing such data. This paper discusses the Fisher-Shannon method, from which one can obtain a complexity measure and detect non-stationarity, as an efficient data exploration tool. The state-of-the-art studies related to the Fisher-Shannon measures are collected, and new analytical formulas for positive unimodal skewed distributions are proposed. Case studies on both synthetic and real data illustrate the usefulness of the Fisher-Shannon method, which can find application in different domains including time series discrimination and generation of times series features for clustering, modeling and forecasting. The paper is accompanied with Python and R libraries for the non-parametric estimation of the proposed measures.
Mots-clé
Fisher-Shannon complexity, Fisher-Shannon information plane, Shannon entropy power, Fisher information measure, statistical complexity, non-linear time series, dynamical behavior characterization, high frequency wind speed
Financement(s)
Fonds national suisse
Création de la notice
14/07/2020 13:05
Dernière modification de la notice
15/07/2020 6:09
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