I am building different forecasting models on my time series (which is not stationary). Some models require stationarity, others don't. My goal is to test a few models on a training set, compare the forecasts to the observed values (the test set), and see which model was the most accurate in forecasting.
However, as I am differencing my data for some models only, when I'm trying to compare the forecasts I obviously end up with different type of output (differenced vs. undifferenced). How should do we usually proceed at this point? Is there a way to revert back my forecasts to "undifferenced" data (in R)?
In know that with ARIMA, it's easy as I can simply ask ARIMA to difference it for me. But when I difference manually using
diff()before forecasting using
ETS(), then it's a different story. I only difference once by the way.