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Online Localized Conformal Prediction

NeurIPS 2026 · Learning theory and algorithms › Prediction and decision losses · approach: Conformal prediction sets · 0 citations (OpenAlex)

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Proposes Online Localized Conformal Prediction (OLCP), combining kernel-localized conformal quantiles with ACI-style online level updates, plus OLCP-Hedge, which selects bandwidths via constrained online convex optimization over experts.

Problem

Online conformal prediction for non-exchangeable time series is globally calibrated and thus inefficient under covariate-dependent heterogeneity; localized conformal methods assume exchangeability and are not designed for online streams.

Approach

Localize the online ACI calibration by weighting recent calibration scores by covariate similarity (kernel with bandwidth h), update the nominal level with coverage feedback, and aggregate bandwidth experts with a constrained OCO (virtual queue + AdaHedge) that minimizes set size subject to long-run coverage.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: ELEC2 ILINet ETF volatility (SPY, QQQ, IWM, EEM, TLT with VIX)

Benchmarks: simulated AR scenarios A/B/C ELEC2 ILINet ETF volatility

Metrics: empirical marginal coverage average prediction set size running time

Baselines: CP LCP ACI DtACI SPCI

Builds on: adaptive conformal inference localized conformal prediction DtACI AdaHedge Sinha-Vaze constrained OCO

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