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Slope Entropy Characterisation: An Asymmetric Approach to Threshold Parameters Role Analysis
Mahdy Kouka1, David Cuesta-Frau1,2, Vicent Moltó-Gallego1
1Department of System Informatics and Computers, Universitat Politècnica de València, 03801 Alcoy, Spain.
Entropy (Basel, Switzerland)
|January 22, 2024
Summary
Optimizing Slope Entropy (SlpEn) by exploring threshold parameters (δ and γ) enhances time series classification accuracy. An asymmetric threshold scheme and grid search improve performance but increase computational cost.
Area of Science:
- Time Series Analysis
- Signal Processing
- Computational Neuroscience
Background:
- Slope Entropy (SlpEn) is a recent time series entropy estimation method.
- It uses embedded dimension (m) and two thresholds (δ and γ) for symbolic representation.
- Existing research has explored δ, but the role of γ and asymmetric thresholds requires further investigation.
Purpose of the Study:
- To investigate the impact of the γ threshold on SlpEn.
- To explore asymmetric threshold schemes for SlpEn.
- To compare standard SlpEn with an optimized version for signal classification.
Main Methods:
- Comparative analysis of standard SlpEn and an optimized version.
- Grid search optimization to maximize signal classification performance.
- Investigation of asymmetric threshold selection for SlpEn parameters.
Main Results:
- Optimized SlpEn achieved higher time series classification accuracy.
- The study confirmed the significant role of the γ threshold.
- Asymmetric threshold schemes were explored for potential benefits.
Conclusions:
- Optimizing SlpEn parameters, particularly γ, improves classification performance.
- The optimized method offers enhanced accuracy at the expense of increased computational complexity.
- Further research into SlpEn threshold optimization is warranted for advanced time series analysis.
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