Missing data are a part of almost all research and it must be decided how to deal with it from time to time. Missing data creates several problems in many applications which depend on good access to accurated data. Conventional methods for missing data, like listwise deletion or regression imputation, are prone to three serious problems: Inefficient use of the available information, leading to low power and Type II errors. Biased estimates of standard errors, leading to incorrect p-values. Biased parameter estimates, due to failure to adjust for selectivity in missing data. In this study, we propose a new algorithm to predict missing values of a given time series using Radial Basis Functions.