# Predicting a baseline response using ARIMA forecasts

I have a modelling dilemma. I am creating a model that attempts to predict demand (leads not sales) based upon the correlation to advertising spend. We know that without advertising spend, demand is driven by seasonality. So our models include seasonal factors like month of the year and even day of the week. If I were building a regular linear regression model, I would fit a linear regression model to a training dataset, to get estimates of the coefficients of the seasonal factors and advertising spend to demand. In order to get an estimate of future baseline demand, I would forecast demand using all the coefficients from the model and then I would estimate a baseline by setting adspend equal to zero. For ARIMA models, there are additional factors such as AR and MA terms. Would I estimate my baseline the same way by just setting the coefficient on advertising spend equal to zero? Thanks for any thoughts.