  Covariant Lyapunov vectors characterize the directions along which
perturbations in dynamical systems grow. They have also been studied as
predictors of critical transitions and extreme events. For many applications
like, for example, prediction, it is necessary to estimate the vectors from
data since model equations are unknown for many interesting phenomena. We
propose a novel method for estimating covariant Lyapunov vectors based on data
records without knowing the underlying equations of the system. In contrast to
previous approaches, our approach can be applied to high-dimensional data-sets.
We demonstrate that this purely data-driven approach can accurately estimate
covariant Lyapunpov vectors from data records generated by low and
high-dimensional dynamical systems. The highest dimension of a time-series from
which covariant Lyapunov vectors were estimated in this contribution is 128.
Being able to infer covariant Lyapunov vectors from data-records could
encourage numerous future applications in data-analysis and data-based
predictions.
