The stock market value of the data point connected by the red line is predicted by linear regression using market values as well as Twitter sentiment data and more in a certain period of time (red area).
Ideally, this should result in some form of uncertainty quantification, i.e.: the further the model tries to predict from the known state of the market, the more uncertain the predicted value becomes.
Currently my model predicts the same change of market value no matter how far ahead we are trying to predict.
Here is an visualization of two data points: features from the red zone with the market value difference as label.
What is a good way of finding some kind of uncertainty quantification depending on how far ahead we predict?
Adding a feature "days_ahead" to give some sort of prediction window seems too simplistic, because I do not believe that my model will understand the meaning of it. How can I incorporate this information?