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

This addresses regression problems for scenarios with dependent data, but it appears incremental as it builds on existing neural network methods with specific assumptions.

The authors tackled regression with dependent data by introducing regularity assumptions and structural conditions, showing that a deep recurrent neural network estimate can avoid the curse of dimensionality.