  This paper proposed a new method to estimate the missing data by using the
filtering process. We used datasets without missing data and randomly missing
data to evaluate the new method of estimation by using the Box - Jenkins
modeling technique to predict monthly average rainfall for site 5504035 Lahar
Ikan Mati at Kepala Batas, P. Pinang station in Malaysia. The rainfall data was
collected from the $1^{st}$ January 1969 to $31^{st}$ December 1997 in the
station. The data used in the development of the model to predict rainfall were
represented by an autoregressive integrated moving - average (ARIMA) model. The
model for both datasets was ARIMA$(1,0,0)(0,1,1)_s$. The result checked with
the Naive test, which is the Thiel's statistic and was found to be equal to
$U=0.72086$ for the complete data and $U=0.726352$ for the missing data, which
mean they were good models.
