When forecasting time series, how does one incorporate the test data back into the model after training?
I agree with what Emre has commented on your question.

If you have enough data, I would try cross-validating your model by training it on different time sections of your data. For example, train your model on Jan-March data and then make predictions on April's data. Then you could train the model on Feb-April data, and predict May's data. Essentially creating a few training and testing sets to see how well your model generalizes. Once you feel you are getting good results, you could train your final "production" model on all of your available data and then keep an eye on how it does when real data comes in and make adjustments on what you see.

I've used a similar approach in the past on accelerometer data to classify exercises from a specific workout, and it worked well. Maybe something similar will work for you.