  We show how to construct the optimum superstatistical dynamical model for a
given experimentally measured time series. For this purpose we generalise the
superstatistics concept and study a Langevin equation with a memory kernel
whose parameters fluctuate on a large time scale. It is shown how to construct
a synthetic dynamical model with the same invariant density and correlation
function as the experimental data. As a main example we apply our method to
velocity time series measured in high-Reynolds number turbulent Taylor-Couette
flow, but the method can be applied to many other complex systems in a similar
way.
