I have a time-series of daily measurements of some quantity for 1995-2011. There's about one measurement every three days.
The data show a strong seasonality (annual cycle).
What I ultimately want is get an estimation for the underlying trend. STL decomposition works fine and gives me seperate trend, seasonality, and noise components of my original time series. But how do I get a quantitative measure for the trend component? A simple linear regression won't do due to the autocorrelation in the noise component. Gavin Simpson suggested using
gls() from R's nlme package, but I'm not sure how I should do that. Which would be the inputs for
Or, are there other suggestions for tackling my problem?
Any help is greatly appreciated :)