  The aim this study is discussed on the detection and correction of data
containing the additive outlier (AO) on the model ARIMA (p, d, q). The process
of detection and correction of data using an iterative procedure popularized by
Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA
models were fit to the data containing AO, this model is added to the original
model of ARIMA coefficients obtained from the iteration process using
regression methods. This shows that there is an improvement of forecasting
error rate data.
