Exploring the Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps
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References listed on IDEAS
- Li, Sange & Shang, Pengjian, 2021. "Analysis of nonlinear time series using discrete generalized past entropy based on amplitude difference distribution of horizontal visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
- Ahmed Sedik & Ahmed A. Abd El-Latif & Mudasir Ahmad Wani & Fathi E. Abd El-Samie & Nariman Abdel-Salam Bauomy & Fatma G. Hashad, 2023. "Efficient Multi-Biometric Secure-Storage Scheme Based on Deep Learning and Crypto-Mapping Techniques," Mathematics, MDPI, vol. 11(3), pages 1-26, January.
- Ömer Akgüller & Mehmet Ali Balcı & Larissa M. Batrancea & Lucian Gaban, 2023. "Path-Based Visibility Graph Kernel and Application for the Borsa Istanbul Stock Network," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
- Li, Sange & Shang, Pengjian, 2022. "A new complexity measure: Modified discrete generalized past entropy based on grain exponent," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
- Gao, Meng & Ge, Ruijun, 2024. "Mapping time series into signed networks via horizontal visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
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Keywords
chaotic maps; NNetEn; neural network entropy; horizontal visibility graphs; fuzzy entropy; classification; entropy global efficiency; GEFMCC; Python;All these keywords.
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