Symbolic transfer entropy.

By Matthäus Staniek, Klaus Lehnertz. Physical Review Letters, 2008

547 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/a0d249778bc326dd3351017fc0ba8b46c7733b4e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Luciano Zunino
2. Jürgen Kurths
3. Yong Zou
4. Tiago Pereira
5. Aneta Stefanovska
6. Peter V. E. McClintock
7. Joseph Troy Lizier
8. Jakob Runge

In Cognitive Neuroscience

1. Yong Zou
2. Norbert Marwan
3. Jonathan F. Donges
4. Reik V. Donner
5. Jürgen Kurths
6. Tiago Pereira
7. Aneta Stefanovska
8. Peter V. E. McClintock

In Economics and Econometrics

1. Norbert Marwan
2. Jürgen Kurths
3. Yong Zou
4. Reik V. Donner
5. Jonathan F. Donges
6. Luciano Zunino
7. Osvaldo Anibal Rosso
8. Tiago Pereira

Citations in the corpus

• JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems (2014, methodology): “Permutation entropy has, for example, been adapted to estimate TE as the symbolic transfer entropy (Staniek and Lehnertz, 2008).”
• Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review (2012, background): “Staniek et al. [81] combined transfer and permutation entropy, to analyze electroencephalographic recordings from 15 epilepsy patients.”
• Causal network reconstruction from time series: From theoretical assumptions to practical estimation. (2018, methodology): “Many current methods are advancements of the concept of transfer entropy (Wibral et al., 2013; Staniek and Lehnertz, 2008; Vejmelka and Palus, 2008), in particular in its multivariate version (Sun and Bollt, 2014; Sun et al., 2015; Runge et al., 2012b; 2012a; Runge, 2015).”
• Complex network approaches to nonlinear time series analysis (2019, background): “Based upon the accordingly discretized time series, a variety of relevant dynamical properties can be conveniently estimated, including symbolic correlation functions [329], mutual information [330], permutation entropy [70], or transfer entropy [331].”
• Coupling functions:universal insights into dynamical interaction mechanisms (2017, methodology): “…have been designed (Bahraminasab et al., 2008; Chicharro and Andrzejak, 2009; Faes et al., 2011; Jamšek et al., 2010; Marwan et al., 2007; Mormann et al., 2000; Paluš and Stefanovska, 2003; Rosenblum and Pikovsky, 2001; Smirnov and Bezruchko, 2009; Staniek and Lehnertz, 2008; Sun et al., 2015).”