I forecast monthly spending two years into the future. While our error is generally acceptable (5 percent or so a year out) I want to improve on that. I run exponential smoothing models (six of which I pick the best 3, best being defined by a MAPE from a hold out data set). I am trying to add an ARIMA models although that is experimental. I update my estimates once a month, we get new data only once a month.
No one at HQ, probably no one period, knows our spending process very well and there is no interest at HQ (other than by me) to do so. So applying expert judgement to the process is essentially impossible although I have tried. All we have right now is past data.
Any suggestion to improve this process, would be appreciated.
No one at HQ, probably no one period, knows our spending process very well and there is no interest at HQ (other than by me) to do so. So applying expert judgement to the process is essentially impossible although I have tried. All we have right now is past data.
Any suggestion to improve this process, would be appreciated.