I've developed a reasonable time-series model on first differenced data. The end goal is to be able to forecast an actual value, not a differenced value.
From reading other posts I learned that the first step is to add the prediction of the model to the previous month's value.
The problem that I run into, and haven't been able to find an answer to, is that the trend that I differenced to get rid of has no representation in my predicted values. This causes my prediction to be consistently too high (my data has a negative trend).
I've looked into decomposition to get a trend, but I'm not sure how/if the trend component would be helpful in making my model more accurate.