Are there pragmatic bounds on how far ahead one can use an ARIMA or Exponential Smoothing forecast?
I have 2 years of weekly data, yet the problem I am trying to solve requires a forecast that is 12 months out.
I'm thinking that a simple average of the LY and LLY data:
TS(week 44 year 3) = [TS(week 44 year 2) + TS(week 44 year 1)]/2
would be just a good a guess as whatever ARIMA or ETS would give me.
Is this indeed a more pragmatic approach than a more involved forecasting method?
Some of the data show a potential year to year trend, but are two years enough to decide that the TS is trending in a given direction ?
Best Answer
Generally speaking, as others have said, the further out your forecast the more likely your prediction errors will increase. With 2 years of data, I would not trust either.
As far as your approach, you're essentially using a seasonal naive approach that averages the past 2 corresponding weeks of the previous seasons.
Assuming you know for a fact this is seasonal data, why not just use seasonal naive? With such limited data, you have no way of knowing if you'll gain or lose accuracy by averaging the last two seasonal periods, so you may as well go with the less complex model (unless, of course, you have some domain knowledge that is swaying you to average them).