Regression for forecasting I am trying to fit a regression model to predict the revenue generated from the sales of a particular product. I have found out seasonality both in the outcome(sales revenue) as well as in the input variables. 
Sales Revenue


The 'Seasonally Adjusted Annual Rate' and the 'Non Seasonally Adjusted' version of each of the input variables are available. 


What version should be used to training the model ?  
Should 'month' be included as a categorical variable as a means to capture
seasonality ?
Should the seasonality be removed from revenue if the Seasonally Adjusted version of inputs are used?
If the goal is to predict actual revenue, you want to predict the unadjusted rate. You should include month as a variable so your model has a chance of getting the seasonality right. 

If the goal was more performance monitoring (e.g. "We did better in March than February"), then you would want to be analyzing the adjusted rate. You see this all the time in the US news when they forecast adjusted home sales, car sales, or unemployment.