I have the following case for which I am needing to forecast a value, say, 12 months out:
- Many individual entities each with their own time series. Each entity has the same data structure
- For each series, say 24 - 36 months of historical data exists (each entity is "created" and then "matures")
- Each entity will mature differently (and as of yet, unpredictably) so we'll use multivariate training data to hopefully capture that unknown signal in suspect variables
- We are likely only interested in that 12 month step forecast, and possibly only once (made early on, like 6 months in)
- Forecasts are to be made on new entities based on learning from the old
I'm a bit at a loss as to whether this is better modelled with something like ARIMA/time-series like approaches (which I am less familiar with) or simply multilinear regression with some lagged columns. It seems a mix of the two to me, but is there any way in which either is more appropriate here?