Uncertainty in time series can appear in many ways, and its analysis can be performed based on different theories. An important problem appears when time series is incomplete since the analyst should impute those observations before any other analysis. This chapter focuses on designing an imputation method for multiple missing observations in time series through the use of a genetic algorithm (GA), which is designed for replacing these missed observations in the original series. The flexibility of a GA is used for finding an adequate
