In this thesis, rates of convergence to zero are obtained for the estimation risk, for non-parametric regression using wavelets, when the errors are correlated. Four non-parametric regression methods using wavelets, with unequally spaced design are studied in the presence of correlated errors, that come from stochastic processes. Conditions on the errors and adaptrations to the procedures are presented, so that the estimators achieve quasi-minimax rates of convergence. Whenever is possible, rates of convergence are obtained for the estimators in the domain of the function, under mild conditions on the function to be estimated, on the design and on the error correlation. Through simulation studies, the behavior of some of the proposed methods is evaluated, when used on nite samples. Generally, it is suggested to use one of the studied methods, however applying thresholds by level. Since the estimation of the detail coecients can be dicult in some cases, it is also proposed a general semi-parametric iterative procedure, for wavelet methods in the presence of time-series errors.