RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy | Zendy

Simon Behrendt | Zendy; Thomas Dimpfl | Zendy; Franziska J. Peter | Zendy; David J. Zimmermann | Zendy

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RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy

Author(s) -

Simon Behrendt,

Thomas Dimpfl,

Franziska J. Peter,

David J. Zimmermann

Publication year - 2019

Publication title -

softwarex

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.528

H-Index - 21

ISSN - 2352-7110

DOI - 10.1016/j.softx.2019.100265

Subject(s) - transfer entropy , computer science , entropy (arrow of time) , series (stratigraphy) , inference , information transfer , statistical inference , data mining , artificial intelligence , principle of maximum entropy , mathematics , statistics , physics , thermodynamics , paleontology , telecommunications , biology

This paper shows how to quantify and test for the information flow between two time series with Shannon transfer entropy and Renyi transfer entropy using the R package RTransferEntropy . We discuss the methodology, the bias correction applied to calculate effective transfer entropy and outline how to conduct statistical inference. Furthermore, we describe the package in detail and demonstrate its functionality by means of several simulated processes and present an application to financial time series.

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