I have information about past and future values that I want to incorporate into my timeseries model (experimenting with ARIMA and other models) in order to predict the future at a more granular timescale. I'm struggling with how to include it.
For example: I want to forecast ice cream sales at my store, weekly, for the next year, and I have weekly data for the last couple of years. I know the % growth for the past couple of years on an annual basis (say, 2%, 3% and 3.5% for the last three years), and I predict the coming year's growth to be 6%.
Assuming I'm pretty confident in my prediction of 6% growth for the coming year (maybe I plan to ramp up advertising throughout the year etc. but can't really say for sure that the 2%, 3%, and 3.5% in the past were entirely due to advertising), I want to incorporate this information (but, I'm not very good at predicting growth on a weekly basis so I don't have weekly growth predictions).
But, including growth as an external regressor doesn't seem to make sense, because (past) growth isn't a separate factor/regressor - it's just the last three years of weekly data (that are already in the model) presented on an annual basis.
Assuming I still want to incorporate the information from my model (growth patterns within a year, high seasonality in ice cream sales), should I just manually adjust the forecast after creating it to force next year's total to equal 6% growth? If so, is there a canonical way to do this? For example, if I calculate that my model forecasted 4% annual growth for the coming year, I could just add a small percentage to each week to make up the difference between the 4% the model forecasts the 6% I predict myself. I would also need to consider how to distribute those small percentages across weeks, as annual growth likely doesn't abruptly change from 3.5% to 6% on the first day of the coming year.