Time series is a sequence of observations of one or a few time variable of a dynamical system. Linear analysis assumes that the intrinsic dynamics of the system is related to the fact that small causes lead to small effects. On the other hand, nonlinear data set may be related to irregular data with purely deterministic inputs. Nonlinear time series analysis is of special interest of several areas. Time series prediction is an area of this general topic that has the objective of estimating future values from a known time series, called past, without any knowledge of the governing equations of phenomena. This article considers the analysis of some prediction techniques applied to time series obtained from an experimental nonlinear pendulum. Noise suppression is not contemplated and all signals are analyzed without filtering. Periodic and chaotic signals are analyzed employing three different predictors: simple nonlinear, polynomial and radial basis functions. The influence of state space reconstruction is exploited showing that it is an important task to be taking into account in prediction problems.