Hi all,



I have a time series that contains double seasonal components (48 and 336) and I would like to decompose the series into the following time series components (trend, seasonal component 1, seasonal component 2 and irregular component). As far as I know, the STL procedure for decomposing a series in R only allows one seasonal component, so I have tried decomposing the series twice. First, by setting the frequency to be the first seasonal component using the following code:

ser = ts(data, freq=48)
dec_1 = stl(ser, s.window="per")

Then, I decomposed the irregular component of the decomposed series (dec_1) by setting the frequency to be the second seasonal component, such that:

ser2 = ts(dec_1$time.series[,3], freq=336)
dec_2 = stl(ser2, s.window="per")

I'm not very confident with this approach. And I would like to know if there are any other ways to decompose a series that has multiple seasonalities. Also,I have noticed that the tbats() function in the R forecast<http://cran.r-project.org/web/packages/forecast/index.html> package () allows one to fit a model to a series with multiple seasonalities.



Basically, i would like the STL result to be components of Figure 5 on Page 28 of this article (http://robjhyndman.com/papers/complex-seasonality/). However, it doesn't say how to implement (the decomposition in R. Does anyone know how to apply the tbats() function in R for decomposition?



Regards

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