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A study for multiscale information transfer measures based on conditional mutual information

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  • Xiaogeng Wan
  • Lanxi Xu

Abstract

As the big data science develops, efficient methods are demanded for various data analysis. Granger causality provides the prime model for quantifying causal interactions. However, this theoretic model does not meet the requirement for real-world data analysis, because real-world time series are diverse whose models are usually unknown. Therefore, model-free measures such as information transfer measures are strongly desired. Here, we propose the multi-scale extension of conditional mutual information measures using MORLET wavelet, which are named the WM and WPM. The proposed measures are computational efficient and interpret information transfer by multi-scales. We use both synthetic data and real-world examples to demonstrate the efficiency of the new methods. The results of the new methods are robust and reliable. Via the simulation studies, we found the new methods outperform the wavelet extension of transfer entropy (WTE) in both computational efficiency and accuracy. The features and properties of the proposed measures are also discussed.

Suggested Citation

  • Xiaogeng Wan & Lanxi Xu, 2018. "A study for multiscale information transfer measures based on conditional mutual information," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-30, December.
  • Handle: RePEc:plo:pone00:0208423
    DOI: 10.1371/journal.pone.0208423
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    1. Michael Wibral & Nicolae Pampu & Viola Priesemann & Felix Siebenhühner & Hannes Seiwert & Michael Lindner & Joseph T Lizier & Raul Vicente, 2013. "Measuring Information-Transfer Delays," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-19, February.
    2. Paluš, M. & Dvořak, I. & David, I., 1992. "Spatio-temporal dynamics of human EEG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 185(1), pages 433-438.
    3. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    4. J. T. Lizier & M. Prokopenko, 2010. "Differentiating information transfer and causal effect," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(4), pages 605-615, February.
    5. Shi-Hang Yu & De-Hui Wang & Kun Li & Zhi-Wen Zhao, 2017. "Estimation in autoregressive models with surrogate data and validation data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(3), pages 1532-1545, February.
    6. Fatimah Abdul Razak & Henrik Jeldtoft Jensen, 2014. "Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-14, June.
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