On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data

Abstract

A regression problem with dependent data is considered. Regularity assumptions on the dependency of the data are introduced, and it is shown that under suitable structural assumptions on the regression function a deep recurrent neural network estimate is able to circumvent the curse of dimensionality.