I'm still trying to expand my statistics and forecasting technique knowledge.
Right now I'm forecasting seasonal contact patterns, so the simplest model I can understand with seasonality is a Holt-Winters/ Triple exponential smoothing model.
For many of our decisions, we not only need predictions for one month out, but also two months out, and even as far as 6 months out, or more.
Obviously accuracy increasingly goes out the window as you try to forecast 6 months out, but, I guess we do what we can.
The first thing I immediately noticed after creating the triple exponential smoothing model and initiated the seasonal/ trend values -- is that there are certainly different 'optimal' alpha (level), beta (trend) and gamma (seasonality) values, depending on whether you are forecasting one month out, or 6 months out.
As you might have guessed, the 1-month forecast has far greater accuracy for a test data set with a higher alpha value (stronger recent values) -- and the 6 month is better with a lower alpha value.
Not that it matters much, but the 2 month is closer to the 1 month model, and the 3-4-5 seems to be closer to the 6 month, but they all have their local 'optimal' values.
So if I need a rolling forecast for the next 6 months --- am I to use one model, or am I supposed to use a different set of alpha-beta-gamma values depending on how far out the predicted value is?
I have a minor background in statistics, but I know it's easy to misuse/ misread them. I don't know if using two or three different models like this can lead to mistakes.