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This paper proposed a new method to estimate the missing data by using thefiltering process. We used datasets without missing data and randomly missingdata to evaluate the new method of estimation by using the Box - Jenkinsmodeling technique to predict monthly average rainfall for site 5504035 LaharIkan Mati at Kepala Batas, P. Pinang station in Malaysia. The rainfall data wascollected from the $1^{st}$ January 1969 to $31^{st}$ December 1997 in thestation. The data used in the development of the model to predict rainfall wererepresented by an autoregressive integrated moving - average (ARIMA) model. Themodel for both datasets was ARIMA$(1,0,0)(0,1,1)_s$. The result checked withthe 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, whichmean they were good models.

Estimation of missing data by using the filtering process in a time series modeling