We propose a new approach to dynamical system forecasting called data-informed-reservoir
computing (DI-RC) that, while solely being based on data, yields increased accuracy …

A reservoir computer (RC) is a type of recurrent neural network architecture with
demonstrated success in the prediction of spatiotemporally chaotic dynamical systems. A …

Reservoir computing approximation and generalization bounds are proved for a new
concept class of input/output systems that extends the so-called generalized Barron …

Reservoir computing (RC) is a simple and efficient model-free framework for forecasting the
behavior of nonlinear dynamical systems from data. Here, we show that there exist …

Numerical evaluations of the memory capacity (MC) of recurrent neural networks reported in
the literature often contradict well-established theoretical bounds. In this paper, we study the …

Macroeconomic forecasting has recently started embracing techniques that can deal with
large-scale datasets and series with unequal release periods. Mixed-data sampling (MIDAS) …

Many recurrent neural network machine learning paradigms can be formulated using state-
space representations. The classical notion of canonical state-space realization is adapted …

The celebrated Takens' embedding theorem concerns embedding an attractor of a
dynamical system in a Euclidean space of appropriate dimension through a generic delay …

The task of modeling and forecasting a dynamical system is one of the oldest problems, and
it remains challenging. Broadly, this task has two subtasks: extracting the full dynamical …

Using short histories of observations from a dynamical system, a workflow for the post-
training initialization of reservoir computing systems is described. This strategy is called cold …