  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.
