  In this study, we investigate the effect of reservoir computing training data
on the reconstruction of chaotic dynamics. Our findings indicate that a
training time series comprising a few periodic orbits of low periods can
successfully reconstruct the Lorenz attractor. We also demonstrate that biased
training data does not negatively impact reconstruction success. Our method's
ability to reconstruct a physical measure is much better than the so-called
cycle expansion approach, which relies on weighted averaging. Additionally, we
demonstrate that fixed point attractors and chaotic transients can be
accurately reconstructed by a model trained from a few periodic orbits, even
when using different parameters.
