I made a time series decomposition with tbats. There is weekly and yearly seasonality in the data (and maybe also monthly - not really important for the question)
x <- msts(data, start=c(2005,1,1), seasonal.period=c(7,30.4,365.25))
fit <- tbats(x, use.box.cox=FALSE)
plot(fit)
As far as i understand the tbats result is an "additive decomposition", right?
I can now access the different parts of the tbats decomposition:
level <- as.numeric(tbats.components(fit)[,'level'])
season1 <- as.numeric(tbats.components(fit)[,'season1'])
season2 <- as.numeric(tbats.components(fit)[,'season2'])
season3 <- as.numeric(tbats.components(fit)[,'season3'])
Since i suppressed 'Box-Cox transformation' earlier, level is roughly the same then trend according to a post from Rob J Hyndman in the comments section here: here
In order to get the remainder part of the decomposition is it a legit way to just subtract all the parts from the original data?
remainder <- data - level - season1 - season2 - season3
What do i get when i use this:
y <- resid(fit)
I am a bit confused right now about the right way to do it... Many thanks for all your input!
Update:
resid(fit)
fit$errors
Those two are both the same. I guess these values are related to the tbats method. I am doubting that i can take them as the "remainder" of the decomposition? Is there a way to extract the remainder out of the tbats method? In his paper 'Forecasting time series with complex seasonal patterns using exponential smoothing' Rob J Hyndman shows remainder graphs for the tbats method so that's why i think it is possible to get in R as well. Anyone any thoughts about that?