I have two time series, first is forecasted values (results of some forecasting algorithm) and second series is, actual values observed for same time frame.
We are trying to compare both these series and find out how much forecasted values are inline with actual values. We have used MAPE & MAE formulas which are fine when the difference between both are small values.
When difference between actual and forecast values are far off, MAE and MAPE are bigger values (I understand that MAPE has no upper limit, which is why bigger values).
These values make sense for statistician, but when common users saw these numbers, feedback we got was our accuracy calculation is doomed. Now the task we are trying to answer is, how can we calculate the difference between forecast vs actual (using MAE (or) MAPE (or) some other algorithm) and show it with in range of
0 - 100%?
Any suggestions would be appreciated.
EDIT 1: These numbers are handmade to convey the problem we are trying to solve, please don't consider about what forecasting algorithm may improve our forecast values etc., irrespective what best algorithm we use, there are few data sets we have could force us into this particular situation.
Here is example time series: