Conejero, J.A.; Velichko, A.; Garibo-i-Orts, Ò.; Izotov, Y.; Pham, V.-T. Exploring the Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps. Mathematics2024, 12, 938.
Conejero, J.A.; Velichko, A.; Garibo-i-Orts, Ò.; Izotov, Y.; Pham, V.-T. Exploring the Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps. Mathematics 2024, 12, 938.
Conejero, J.A.; Velichko, A.; Garibo-i-Orts, Ò.; Izotov, Y.; Pham, V.-T. Exploring the Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps. Mathematics2024, 12, 938.
Conejero, J.A.; Velichko, A.; Garibo-i-Orts, Ò.; Izotov, Y.; Pham, V.-T. Exploring the Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps. Mathematics 2024, 12, 938.
Abstract
In this paper, we propose a method for assessing the effectiveness of entropy features using several chaotic mappings. We anlyze fuzzy entropy (FuzzyEn) and neural network entropy (NNetEn) on four discrete mappings: the logistic map, the sine map, the Planck map, and the two-memristor-based map, with a base length time series of 300 elements. FuzzyEn is shown to have improved global efficiency (GEFMCC) in the classification task compared to NNetEn. At the same time, there are local areas of the time series dynamics in which the classification efficiency NNetEn is higher than FuzzyEn. The results of using horizontal visibility graphs (HVG) instead of the raw time series are also shown. GEFMCC decreases after HVG time series transformation, but there are local areas of the time series dynamics in which the classification efficiency increases after including the HVG. The scientific community can use the results to explore the efficiency of entropy-based classification of time series.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
