Practical Advantages of Online Conformal Prediction for Data Streams

Key points

• We compare four sequential conformal regression procedures with a weighted full-conformal benchmark in terms of empirical coverage, prediction interval width, and computational cost.1
• The procedures combine ordinary or forgetting-factor least squares with uniform or exponentially weighted empirical residual quantiles, or with online gradient-based quantile tracking.1
• Adapting the regression model as well as the conformal threshold substantially improves the coverage--width tradeoff under drift.1
• 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.1

Sources (1)

• [1]Practical Advantages of Online Conformal Prediction for Data Streams
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 07:04 AM

  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.

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