in forecasting, typically, we assign a heavier weight to the most current observations. However, I am finding many cases where a "blip" in last month's sales leads to a very pessimistic view about the future 12 months. Blips also lead to wide swings when comparing a forecast generated before the blip vs the forecast updated next month (after the blip happened).
So, my question is whether it is possible to take a look at last month's data within the context of a longer series of observations, and reduce the weight of the most current observation if this deviates substantially from the longer term trend.
I use to consider ARIMA, HoltWinters, TBATS, STL, ETS etc among my model candidates in R, but I haven't found any reference to this being doable in an automated way.
Any ideas that you can think of?