Novel Features for Binary Time Series Based on Branch Length Similarity Entropy
Sang-Hee Lee1, Cheol-Min Park1
1Division of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon 34047, Korea.
Entropy (Basel, Switzerland)
|April 30, 2021
Summary
This study introduces Branch Length Similarity (BLS) entropy for binary time-series analysis by mapping signals to a time circle. Characteristic features derived from the BLS entropy profile offer new insights into signal dynamics.
Area of Science:
- Complex systems analysis
- Information theory
- Time-series analysis
Background:
- Branch Length Similarity (BLS) entropy is a measure for networks.
- Binary time-series data present unique analytical challenges.
Purpose of the Study:
- To adapt BLS entropy for binary time-series analysis.
- To identify and explore characteristic features of binary time-series using BLS entropy.
- To demonstrate the applicability of these features in biological data.
Main Methods:
- Mapping binary time-series signals onto a time circle.
- Calculating BLS entropy for "1" signals to generate an entropy profile.
- Identifying local maximum/minimum points, slope, and inflection points of the entropy profile as characteristic features.
Main Results:
- The local maximum/minimum point signifies zero rate of change in signal density.
- Slope and inflection points correlate with the degree and timing of signal density changes.
- Characteristic features successfully characterized the movement trajectory of Caenorhabditis elegans.
Conclusions:
- BLS entropy provides a novel method for binary time-series analysis.
- Identified features offer significant insights into signal dynamics.
- The methodology shows broad applicability, including biological movement analysis.
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