arXiv:2610.27740v1 Announce Type: new Abstract: Full conformal prediction offers finite-sample marginal coverage guarantees under exchangeability, but exact computation can be costly, while its classical guarantees do not directly extend to non-stationary data streams. We compare four sequential conformal regression procedures with a weighted full-conformal benchmark in terms of empirical coverage, prediction interval width, and computational cost. The procedures combine ordinary or forgetting-factor least squares with uniform or exponentially weighted empirical residual quantiles, or with online gradient-based quantile tracking. Experiments cover an independent and identically distributed control, two synthetic distribution-shift scenarios, and four real-world data streams. Adapting the regression model as well as the conformal threshold substantially improves the coverage--width tradeoff under drift. The quantile-tracking procedure requires $O(p^2)$ time and memory per observation, independently of stream length, although its coverage can deteriorate after abrupt changes. These results illustrate the practical tradeoffs between predictor adaptation, uncertainty quantification, and computational resources.