Efficient computation and statistical assessment of transfer entropy

The analysis of complex systems frequently poses the challenge to distinguish correlation from causation. Statistical physics hasinspired very promising approaches to search for correlations in time series; the transfer entropy in particular (Hlavackova-Schindler et al., 2007). Now, methods from...

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Bibliographic Details
Published in:Frontiers in Physics
Main Authors: Patrick eBoba, Dominik eBollmann, Daniel eSchoepe, Nora eWester, Jan eWiesel, Kay eHamacher
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-03-01
Subjects:
Information Theory
complex systems
causality
time series analysis
transfer entropy
bootstrapping
Online Access:http://journal.frontiersin.org/Journal/10.3389/fphy.2015.00010/full
Description
Summary:The analysis of complex systems frequently poses the challenge to distinguish correlation from causation. Statistical physics hasinspired very promising approaches to search for correlations in time series; the transfer entropy in particular (Hlavackova-Schindler et al., 2007). Now, methods from computational statistics can quantitatively assign significance to such correlation measures. In this study, we propose and apply a procedure to statistically assess transfer entropies by one-sided tests. We introduce to null models of vanishing correlations for time series with memory.We implemented them in an OpenMP-based, parallelized C++ package for multi-core CPUs. Using template meta-programming, we enable a compromise between memory and run time efficiency.
ISSN:2296-424X

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