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Slope Entropy Normalisation by Means of Analytical and Heuristic Reference Values.

David Cuesta-Frau1, Mahdy Kouka2, Javier Silvestre-Blanes1

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Summary
This summary is machine-generated.

Slope Entropy (SlpEn) is a novel time series analysis method. This study introduces a normalization technique to bound SlpEn values within [0,1], enhancing its interpretability for time series classification.

Keywords:
entropy normalisationmaximum entropyminimum entropyslope entropytime series classification

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Area of Science:

  • Complexity Science
  • Information Theory
  • Time Series Analysis

Background:

  • Slope Entropy (SlpEn) is a recent entropy calculation method for time series.
  • SlpEn uses consecutive value differences and thresholds to create symbolic patterns.
  • Standard SlpEn outputs often exceed the [0,1] interval, limiting comparability.

Purpose of the Study:

  • To develop a method for normalizing SlpEn values to the [0,1] interval.
  • To improve the interpretability and comparability of SlpEn.
  • To facilitate the application of SlpEn in time series classification and entropy analysis.

Main Methods:

  • A two-step max-min normalization scheme is proposed.
  • An initial analytic normalization uses conservative bounds.
  • Heuristics on pattern counts in deterministic and random series refine these bounds.

Main Results:

  • The proposed method effectively normalizes SlpEn results to the [0,1] interval.
  • The normalization enhances the interpretability and comparability of SlpEn.
  • A combination of analytic and heuristic normalization proves suitable.

Conclusions:

  • The developed normalization method successfully bounds SlpEn within [0,1].
  • This normalization improves SlpEn's utility for comparative entropy analysis and time series classification.
  • The approach offers a standardized framework for SlpEn application.