I have a data set of electricity spot prices, which contains three kinds of seasonality: one within 24 hours, one within a week and one within a year.
I want to use an R package (
tsDyn) which can't cope with seasonality, so first I would like to remove all three seasonalities, then adapt a model to the deseasonalized data, perform a forecast and then add the seasonalities, if it is possible, in order to transform my forecasts to reasonable form.
Is this approach sensible and possible? And if yes, how could I accomplish this triple deseasonalization and then undo it within R? In the case of a simple one lag differencing I would just undo the seasonal differencing with 'cumsum()', but is something like this applicable for my data set?