Transfer Entropy With Partitioning

@article{pollard_physionet_2026, title = {{PhysioNet} as a global platform for biomedical research}, volume = {1}, issn = {3005-0693}, url = {https://doi.org/10.1038/s44360-026-00096-z}, doi = {10.1038/s44360-026-00096-z}, abstract = {Twenty-five years after its first public repository was established, the PhysioNet resource for biomedical and clinical data has enabled tens of thousands of research papers. Further efforts are ongoing to advance secure data sharing, community engagement and scalable research infrastructure.}, number = {8}, journal = {Nature Health}, author = {Pollard, Tom and Moody, Benjamin E. and Lehman, Li-wei H. and Gow, Brian J. and Fernandes, Chrystinne and Xie, Chen and Johnson, Alistair and Mark, Roger G. and Heldt, Thomas}, month = aug, year = {2026}, pages = {792--795},}

Description

This is a repository of MATLAB functions that can estimate transfer entropy (information flow) from one time series to another using a non-parametric partitioning algorithm. Also included is an example data set that the implemented algorithms can be applied to. The functions were tested in MATLAB R2016b on 03 March 2016.

Usage

There are three functions used to calculate transfer entropy between time series using different methods of probability density estimation:

• [T] = transferEntropyKDE(X,Y,t,w,N,bw_coeff)
  • Based on Guassian kernel density estimation.
  • Calls function mdKDE
• [T nPar dimPar]=transferEntropyPartition(X,Y,t,w)
  • Based on the Darbellay-Vajda partitioning algorithm
  • Calls function DVpartition3D
• [T] = transferEntropyRank(X,Y,l,k,t,w,Q)
  • Based on bin counting with fixed and equally-spaced bins.
  • Calls function quantentr

The MAT file 'example_data.mat' contains a 1-D structure array of example data that harnesses an information flow from X to Y at a lag of 2 according to: y(i)=[(1+a)*x(i-2)]^2
Different values of the coupling constant 'a' were simulated, along with associated levels of Laplacian noise given by SNR levels.

• For a demonstration, run 'demoscript.m'. The plotted results should look like figure 1.
• See the individual .m function files for information on how to call them.
• See readme_example_data.txt for information on the example data parameters.
• See Measuring Information Transfer, Physical Review Letters, 85(2):461-464, 2000 for more details on the methods.

Cited by

128 publications cite this project. The most recent are listed below.

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  Yifeng Shan, MengZe Zheng, Haiyan Sun. Frontiers in Public Health, 2026.
• Scale-dependent coupling and directional causality in solar–wind dynamics
  A.O. Adelakun, I.O. Ajayi, A. C. Adebisi. The European Physical Journal Plus, 2026.
• Contagion and systemic risk in the European banking sector: implications for sustainable regulation and financial system resilience
  Jana Zoricakova, Noemi Filickova, Kristína Kočišová. Eurasian economic review :, 2026.
• Quantifying interaction among POD modes in turbulent flows using transfer entropy
  K. Stoļarovs, S. Toosi, L. Yang et al. Journal of Physics Conference Series, 2026.
• Global causal-aware impact modeling for traffic networks with graph-integrated transfer entropy
  Junji Ye, can li, Fangni Zhang. Transportation Research Part C Emerging Technologies, 2026.

View all 128 publications

Citation data from OpenAlex. Papers that use the project without citing it may be missing.