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

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

2008-11-05

arxiv.org/abs/0811.0659v1

Statistics

Methodology

Submitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Estimation of missing data by using the filtering process in a time series modeling does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Estimation of missing data by using the filtering process in a time series modeling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Estimation of missing data by using the filtering process in a time series modeling will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-373081