Randomly distributed embedding making short-term high-dimensional data predictable [Applied Mathematics]

Future state prediction for nonlinear dynamical systems is a challenging task, particularly when only a few time series samples for high-dimensional variables are available from real-world systems. In this work, we propose a model-free framework, named randomly distributed embedding (RDE), to achieve accurate future state prediction based on short-term high-dimensional...

https://ift.tt/2D3T8gU