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- Title
Multi-span transition networks: a new unified framework for analyzing time series.
- Authors
Xie, Jieren; Xu, Guanghua; Chen, Xiaobi; Zhang, Xun; Chen, Ruiquan; Han, Chengcheng; Wu, Qingqiang; Guo, Xiaobing; Zhang, Sicong
- Abstract
The paper seeks to overcome the limitations inherent in traditional transition network methods, which primarily concentrate on transition frequencies between adjacent symbols, neglecting broader transition relationships. We present a novel approach called "multi-span transition network." This method excels at capturing dynamic information within time series by incorporating transitions across higher time-scale patterns. We also propose a conditional entropy measure to assess the complexity of time-series data derived from the multi-span transition network. With expanding dimensionality, the multi-span transition network adeptly discriminates between various types of time series and unveils concealed information. The conditional entropy of the multi-span transition network exhibits a robust correlation with the maximum Lyapunov exponent of the system. The conditional entropy of a multi-span network can distinguish the time series of different states and determine chaos degradation. Employing the multi-span transition network for the classification of epileptic EEG data resulted in a substantial enhancement in accuracy compared to conventional transition network methods. The method is a more general form of the traditional transition network and is more generalizable.
- Subjects
TIME series analysis; LYAPUNOV exponents; MAXIMUM entropy method; TIME complexity; KOLMOGOROV complexity; ENTROPY
- Publication
Nonlinear Dynamics, 2024, Vol 112, Issue 7, p5503
- ISSN
0924-090X
- Publication type
Article
- DOI
10.1007/s11071-024-09342-z