/Questions:
I only have minor suggestions:
1.) In the discussion, it may be worth including a brief discussion on the empirical motivation for a time-varying Q ^ t and S t
, as opposed to a fixed one as in Section 4.2. For example, what is the effect on the volatility of α t
and also on the average lengths of the predictive intervals when we let Q ^ t and S t
vary with time?
2.) I found the definition of the quantile a little confusing, an extra pair of brackets around the term ( 1 | D | ∑ ( X r , Y r ) ∈ D 1 S ( X r , Y r ) ≤ s )
might help, or maybe defining the bracketed term separately if space allows.
3.) I think there are typos in Lines 93, 136, 181 (and maybe in the Appendix too): should it be Q ^ t ( 1 − α t ) instead? ##################################################################### Overall:
This is a very interesting extension to conformal prediction that no longer relies on exchangeability but is still general, which will hopefully lead to future work that guarantees coverage under weak assumptions. I believe the generality also makes this method useful in practice.
The authors have described the limitations of their theory, e.g. having a fixed Q ^
with time.
1.) In the discussion, it may be worth including a brief discussion on the empirical motivation for a time-varying Q ^ t and S t , as opposed to a fixed one as in Section 4.2. For example, what is the effect on the volatility of α t and also on the average lengths of the predictive intervals when we let Q ^ t and S t vary with time?