Time series analysis of real-world measurements is fundamental in natural
sciences and engineering, and machine learning has been recently of great assistance especially
for classification of signals and their understanding. Yet, the underlying system’s nonlinear
response behaviour is often neglected. Recurrence Plot (RP) based Fourier-spectra constructed
through τ-Recurrence Rate (RRτ) have shown the potential to reveal nonlinear traits otherwise
hidden from conventional data processing. We report a so far disregarded eligibility for
signal classification of nonlinear time series by training RESnet-50 on spectrogram images,
which allows recurrence-spectra to outcompete conventional Fourier analysis. To exemplify its
functioning, we employ a simple nonlinear physical flow of a continuous stirred tank reactor,
able to exhibit exothermic, first order, irreversible, cubic autocatalytic chemical reactions, and
a plethora of fast-slow dynamics. For dynamics with noise being ten times stronger than the
signal, the classification accuracy was up to ≈75 % compared to ≈17 % for the periodogram.
We show that an increase in entropy only detected by the RRτ allows differentiation. This
shows that RP power spectra, combined with off-the-shelf machine learning techniques, have
the potential to significantly improve the detection of nonlinear and noise contaminated signals.