  Many finance, physics, and engineering phenomena are modeled by
continuous-time dynamical systems driven by highly irregular (stochastic)
inputs. A powerful tool to perform time series analysis in this context is
rooted in rough path theory and leverages the so-called Signature Transform.
This algorithm enjoys strong theoretical guarantees but is hard to scale to
high-dimensional data. In this paper, we study a recently derived random
projection variant called Randomized Signature, obtained using the
Johnson-Lindenstrauss Lemma. We provide an in-depth experimental evaluation of
the effectiveness of the Randomized Signature approach, in an attempt to
showcase the advantages of this reservoir to the community. Specifically, we
find that this method is preferable to the truncated Signature approach and
alternative deep learning techniques in terms of model complexity, training
time, accuracy, robustness, and data hungriness.
