Giving more importance to under prediction (mean absolute error) than over prediction for forecasting Just curious to hear any thoughts on weighting over prediction in mean absolute error to minimize the penalty since I'm more interested in under prediction, if that makes sense. Basically, I'm forecasting to find optimal staffing needs so I'm not as worried if the model over predicts too much. On the other hand, I do care about under prediction.
I have basically times the absolute error by half (totally arbitrary) to minimize the penalty on over predictions. I'm more seeking to discuss rather than find a solution since I've already implemented something.

You should ideally look up the economics of the situation involved.
What's the actual financial cost of being understaffed by one? Is it a schedule slippage of one month, which cases late entry to market, which decreases lifetime profits by $100,000?
What about understaffing by two? Maybe the problem compounds and you get four months delayed, at a cost of $600,000. Do this for a few points and linearly interpolate.
The cost of overstaffing is usually easier to estimate: it's the fully loaded cost of staff times the error.
As you can guess, these error functions can get quite detailed and complicated. They may even teach you a thing or two about the problem you're trying to solve.
As a word of caution: don't make the error function in the model too complicated. Usually, the prediction uncertainty itself is so large that you can tolerate significant imprecision in the error function.
