Dear all, I have a daily time series for the months April to September (183 says) for a 40 years period. It contain missing values. I would like to extract a seasonal component, trend component and irregular component using an adaptive model.
#Commands for making a time series: timeser<-rnorm(183*40) #Add some missing values: timeser[c(5,77,98,100,105,1000,1340,2001,3277,3278,3279,4004:4009,7000)]<-NA I had a look on the functions decompose and stl. But it seems unsuitable for my problem. I appreciate if someone can help me out! -- View this message in context: http://www.nabble.com/Decompose-an-irregular-daily-time-series-with-missing-values-tf4346265.html#a12382402 Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
