Ordinal spectrum: a frequency domain characterization of complex time series
Data Analysis, Statistics and Probability
2020-09-08 v1
Abstract
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.
Keywords
Cite
@article{arxiv.2009.02547,
title = {Ordinal spectrum: a frequency domain characterization of complex time series},
author = {Mario Chavez and Johann H. Martinez},
journal= {arXiv preprint arXiv:2009.02547},
year = {2020}
}