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

Xiaogeng Wan1, Lanxi Xu1

  • 1Department of Mathematics, College of Science, Beijing University of Chemical Technology, Beijing, China.

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|December 7, 2018
PubMed
Summary
This summary is machine-generated.

We introduce wavelet-based multi-scale conditional mutual information (WM and WPM) for analyzing complex time series data. These novel methods efficiently quantify information transfer across multiple scales, outperforming existing techniques.

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Area of Science:

  • Data Science
  • Time Series Analysis
  • Information Theory

Background:

  • Granger causality is a standard for causal interaction but struggles with unknown models in real-world diverse time series.
  • Model-free information transfer measures are crucial for analyzing complex, real-world data.

Purpose of the Study:

  • To develop efficient, model-free methods for quantifying multi-scale information transfer in diverse time series.
  • Introduce wavelet-based extensions of conditional mutual information (WM and WPM) for enhanced data analysis.

Main Methods:

  • Utilized MORLET wavelet for a multi-scale extension of conditional mutual information.
  • Developed two novel measures: Wavelet-based Mutual Information (WM) and Wavelet-based Partial Mutual Information (WPM).
  • Validated methods using synthetic data and real-world examples.

Main Results:

  • WM and WPM demonstrate computational efficiency and interpret information transfer across multiple scales.
  • New methods show robust and reliable results in both synthetic and real-world data analyses.
  • Simulation studies indicate WM and WPM outperform the wavelet extension of transfer entropy (WTE) in efficiency and accuracy.

Conclusions:

  • The proposed WM and WPM measures offer a powerful, efficient, and reliable approach for multi-scale information transfer analysis.
  • These methods address limitations of traditional causality models in handling complex, real-world time series.
  • Further discussion on the properties and features of WM and WPM is provided.