2020/09/05 by Mario Chávez, Mario Chavez, Chavez, Mario +3
Biochemistry, Genetics and Molecular Biology · Economics, Econometrics and Finance · Physics and Astronomy · #Chaos control and synchronization #Complex Systems and Time Series Analysis #Data Analysis #FOS: Physical sciences #Fractal and DNA sequence analysis #Statistics and Probability (physics.data-an) #physics.data-an
paper · pdf · doi:10.48550/arxiv.2009.02547
arxiv created 2020/09/05 · openalex publication_date 2020/09/05 · arxiv updated 2020/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Although classical spectral analysis is a natural approach to characterise linear systems, it cannot describe a chaotic dynamics. Here, we propose the ordinal spectrum, a method based on a spectral transformation of symbolic sequences, to characterise the complexity of a time series. In contrasts with other nonlinear mapping functions (e.g. the state-space reconstruction) the proposed representation is a natural approach to distinguish, in a frequency domain, a chaotic behavior. We test the method in different synthetic and real-world data. Our results suggest that the proposed approach may provide new insights into the non-linear oscillations observed in different real data.