I need to automate time-series forecasting, and I don't know in advance the features of those series (seasonality, trend, noise, etc).
My aim is not to get the best possible model for each series, but to avoid pretty bad models. In other words, to get small errors every time is not a problem, but to get big errors once in a while is.
I thought I could achieve this by combining models calculated with different techniques.
That is, although ARIMA would be the best approach for a specific series, it may not be the best for another series; the same for exponential smoothing.
However, if I combine one model from each technique, even if one model isn't so good, the other will bring the estimate closer to the real value.
It is well-known that ARIMA works better for long-term well-behaved series, while exponential smoothing stands out with short-term noisy series.
- My idea is to combine models generated from both techniques in order to get more robust forecasts, does it make sense?
There might be many ways to combine those models.
- If this is a good approach, how should I combine them?
A simple mean of forecasts is an option, but maybe I could get better predictions if I weight the mean according to some goodness measure of the model.
- What would be the treatment of the variance when combining models?