  We consider nonparametric functional regression when both predictors and
responses are functions. More specifically, we let $(X_1,Y_1),...,(X_n,Y_n)$ be
random elements in $\mathcal{F}\times\mathcal{H}$ where $\mathcal{F}$ is a
semi-metric space and $\mathcal{H}$ is a separable Hilbert space. Based on a
recently introduced notion of weak dependence for functional data, we showed
the almost sure convergence rates of both the Nadaraya-Watson estimator and the
nearest neighbor estimator, in a unified manner. Several factors, including
functional nature of the responses, the assumptions on the functional variables
using the Orlicz norm and the desired generality on weakly dependent data, make
the theoretical investigations more challenging and interesting.
