@jwithing Yes, it would be good to include daily paid marketing as a co-variate (feature time-series). Your model will learn the impact of that data and will incorporate it in its forecasting.
Here below is a brief illustrated explanation on how it works at a high level:
A model is trained by randomly sampling several training examples from each of the time series in the training dataset. Each training example consists of a pair of adjacent context and prediction windows with fixed predefined lengths. The
context_length hyperparameter controls how far in the past the network can see, and the
prediction_length hyperparameter controls how far in the future predictions can be made.
The following figure represents five samples with
context lengths of 12 hours and
prediction lengths of 6 hours drawn from element i. The feature time series are xi,1,t and ui,2,t (also called co-variates in literature).
To capture seasonality patterns, a model can also automatically feeds lagged values from the target time series. In the example with hourly frequency, for each time index, t = T, the model exposes the zi,t values, which occurred approximately one, two, and three days in the past.