In this paper we consider nonparametric quantile estimation for time series that are time dependent transformations of a stationary Gaussian process with long-range dependence. Nonstationary and nongaussian processes are included in this framework. Time dependent quantiles are estimated by kernel smoothing. Asymptotic results on mean squared error, optimal bandwith and limiting distribution are obtained. Two data examples are presented.