Now that I know why it is important to represent the uncertainty of a model, I would like to know how to best represent uncertainty in a time series forecasting model.
My previous question introduced me to many different kinds of uncertainty measures. Confidence Intervals, Prediction Intervals, Credible Intervals and Standard Errors (there are probably more which weren't covered).
I've been asked to use Confidence Intervals, but every time I read about them, Confidence Intervals are mentioned in the context of the Gaussian Normal distribution (bell shaped). My time series data does not follow the bell shape.
I also read a discussion on the differences between the Confidence and Prediction intervals. It says that Confidence Intervals are narrower than Prediction Intervals, but I'm not sure how this helps me choose which one to use.
My question is: now that I have made some point forecasts on a time series using my model, how do I choose the most appropriate method to measure my model's uncertainty?