Fractionally integrated random processes are introduced and examined as an alternative to short-memory ARIMA models resulting from integer differencing. It is seen that fractionally integrated time series exhibit persistence at long lags of the autocorrelation function that allows for improved forecasting. Truncated and exact methods for synthetic generation are provided, examples are drawn, and a procedure for estimating fractional differencing parameter d is given. All computational algorithms are presented using the SAS System.