  In this paper we propose a general framework to analyze prediction in time
series models and show how a wide class of popular time series models satisfies
this framework. We postulate a set of high-level assumptions, and formally
verify these assumptions for the aforementioned time series models. Our
framework coincides with that of Beutner et al. (2019, arXiv:1710.00643) who
establish the validity of conditional confidence intervals for predictions made
in this framework. The current paper therefore complements the results in
Beutner et al. (2019, arXiv:1710.00643) by providing practically relevant
applications of their theory.
