We propose a nonlinear prediction (NOP) method for functional time series. Conventional methods for functional time series are mainly based on functional principal component analysis or functional regression models. These approaches rely on the stationary or linear assumption of the functional time series. The NOP method employs a nonlinear mapping for functional data that can be directly applied to multivariate functions without any preprocessing step. It is a one-step model that constructs feature space with forecast information, hence it provides a better ground for predicting future trajectories. Compared to the conventional methods, the NOP method avoids calculating covariance functions and enables online estimation and prediction. Three real applications demonstrate the advantages of the NOP method model in predicting air quality, electricity price, and mortality rate.