A Fast Method for Detecting Interdependence between Time Series and Its Directionality
From MaRDI portal
Publication:5064587
DOI10.1142/S0218127421502394zbMath1493.37094OpenAlexW4200075451MaRDI QIDQ5064587
Stefano Euzzor, Riccardo Meucci, Gabriele Paolini, Jean-Mark Ginoux, Fortunato Tito Arecchi, S. Chillemi, Francesco Sarnari, Angelo Di Garbo, Leone Fronzoni
Publication date: 16 March 2022
Published in: International Journal of Bifurcation and Chaos (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0218127421502394
Dynamical systems in biology (37N25) Time series analysis of dynamical systems (37M10) Symbolic dynamics (37B10)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Detecting Causality in Complex Ecosystems
- Simulation study of direct causality measures in multivariate time series
- Measuring synchronization in coupled model systems: a comparison of different approaches
- Extracting knowledge from time series. An introduction to nonlinear empirical modeling
- The synchronization of chaotic systems
- Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients
- PARAMETER SELECTION FOR PERMUTATION ENTROPY MEASUREMENTS
- Nonlinear Time Series Analysis
- Nonlinearity Tests Using the Extrema of a Time Series
- Partitioning networks into clusters and residuals with average association
- Investigating Causal Relations by Econometric Models and Cross-spectral Methods
- Elements of Information Theory
This page was built for publication: A Fast Method for Detecting Interdependence between Time Series and Its Directionality