Full disclosure: I am not a statistician, nor do I claim to be one. I am a lowly IT administrator. Please play gentle with me. :)
I am responsible for collecting and forecasting disk storage use for our enterprise. We collect our storage use monthly and use a simple rolling twelve month linear regression for forecasts (in other words, only the previous twelve months of data are considered when making a projection). We use this information for allocation and capital expense planning, e.g. "Based on this model, we will need to purchase x amount if storage in y months to meet our needs." This all works well enough to suit our needs.
Periodically, we have large one-time movements in our numbers that throws the forecasting off. For example, someone finds 500GB of old backups that aren't needed anymore and deletes them. Good for them for reclaiming the space! However our forecasts are now skewed way off by this large drop in one month. We have always just accepted that a drop like this takes 9-10 months to make its way out of the models, but that can be a really long time if we are entering capital expense planning season.
I'm wondering if there is a way to handle these one-time variances such that the forecasted values aren't impacted as much (e.g. the slope of the line doesn't change as dramatically), but they are taken into account (e.g. a one-time change in the y-value associated with a particular point in time). Our first attempts at tackling this have yielded some ugly results (e.g. exponential growth curves). We do all of our processing in SQL Server if that matters.