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Ensemble Improved Permutation Entropy: A New Approach for Time Series Analysis.

Zhe Chen1,2, Xiaodong Ma1,2, Jielin Fu1,2

  • 1School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.

Entropy (Basel, Switzerland)
|August 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces ensemble improved permutation entropy (EIPE) and multiscale EIPE (MEIPE) for robust time series analysis. These novel entropy quantification methods offer enhanced discriminating power and noise robustness for engineering applications.

Keywords:
data analysisensemble improved permutation entropyfeature extraction

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Area of Science:

  • Engineering
  • Data Science
  • Signal Processing

Background:

  • Entropy quantification is crucial for engineering applications.
  • Existing methods face limitations in parameter dependence, discriminating power, and noise robustness.

Purpose of the Study:

  • Introduce novel algorithms: ensemble improved permutation entropy (EIPE) and multiscale EIPE (MEIPE).
  • Address limitations of traditional entropy measures in time series analysis.

Main Methods:

  • Developed a new symbolization process incorporating permutation relations and amplitude information.
  • Utilized an ensemble technique to minimize parameter selection dependence.
  • Evaluated methods using synthetic and experimental signals.

Main Results:

  • EIPE effectively distinguishes different noise types (white, pink, brown) with fewer samples.
  • EIPE demonstrates potential in differentiating regular and non-regular dynamics.
  • EIPE shows superior robustness against noise compared to existing entropy measures.
  • Proposed methods exhibit enhanced discriminating power in practical applications like EEG and fault diagnosis.

Conclusions:

  • EIPE and MEIPE offer significant improvements over conventional entropy quantification techniques.
  • The novel algorithms provide effective and robust tools for complex time series analysis in engineering.