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Permutation Entropy and Bubble Entropy: Possible interactions and synergies between order and sorting relations.
David Cuesta-Frau1, Borja Vargas2
1Technological Institute of Informatics(ITI), Universitat Politècnica de València, Campus Alcoi, Plaza Ferrándiz y Carbonell, 2, 03801, Alcoi, Spain.
Mathematical Biosciences and Engineering : MBE
|April 3, 2020
Summary
Bubble Entropy (BE) complements Permutation Entropy (PE) in time series analysis. Combining both methods enhances classification accuracy and robustness, revealing a synergistic relationship rather than superiority.
Area of Science:
- Time series analysis
- Complexity science
- Machine learning
Background:
- Permutation Entropy (PE) is a useful time series analysis tool, but has limitations regarding amplitude information and motif ambiguity.
- Improved PE variants address these weaknesses, with Bubble Entropy (BE) focusing on sorting relations to reduce parameter influence.
Purpose of the Study:
- To comparatively assess the classification performance of Bubble Entropy (BE) against Permutation Entropy (PE).
- To investigate potential synergistic effects when combining PE and BE for time series classification.
Main Methods:
- Conducted time series classification tests using both PE and BE on a diverse dataset.
- Employed a clustering algorithm with PE and BE as input features to evaluate combined performance.
Main Results:
- Results indicated a complementary relationship between PE and BE, not a superior/inferior one.
- Simultaneous use of PE and BE as features in a clustering algorithm improved classification accuracy and robustness.
Conclusions:
- The combination of PE and BE offers enhanced performance in time series classification tasks.
- Synergistic application of these entropy measures provides a more robust approach compared to using either method alone.
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