Adaptive Conformal Inference Under Delayed Feedback: Coverage Guarantees and a Delay-to-Memory Diagnostic

• URL: http://arxiv.org/abs/2609.07251v1
• Date: Mon, 07 Sep 2026 09:10:48 GMT
• Title: Adaptive Conformal Inference Under Delayed Feedback: Coverage Guarantees and a Delay-to-Memory Diagnostic
• Abstract summary: We study how the effect of delay depends on the persistence of the residual process.<n>Overall, the results show that the effect of forecast delay is best understood relative to the time scale over which past residual information remains relevant.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive Conformal Inference (ACI) extends conformal prediction to non-exchangeable settings by adjusting the nominal miscoverage level online in response to recent coverage errors. When forecasts are issued with horizon $τ$, however, the outcome needed to evaluate a prediction is observed only $τ$ steps later, so these adaptive updates must rely on delayed feedback. We study this setting and ask how the effect of delay depends on the persistence of the residual process. First, we show that the $τ$-delayed ACI recursion can be decomposed into $τ$ interleaved ACI-like sequences. This representation yields a finite-sample bound on long-run empirical coverage with explicit dependence on $τ$. We also derive an approximate marginal coverage bound that relates coverage deviation to changes in the underlying environment across the forecast horizon and to the adaptation rate $γ$. We then introduce the delay-to-memory ratio $r=τ/L$, where $L$ is the time scale over which the temporal signal driving non-exchangeability decays. Simulation results show that the usefulness of this ratio depends on the form of temporal dependence: it strongly organizes performance under AR(1) dependence, is less predictive of overall performance under GARCH(1,1) and Markov switching, but more clearly characterizes when scale normalization remains useful in those settings. Abrupt mean- and variance-shift experiments further show that the preferred adaptation rate depends on the residual dynamics. Overall, the results show that the effect of forecast delay is best understood relative to the time scale over which past residual information remains relevant.

Related papers

• RAVEN: A Regime-Aware Variable-context Expert Network for Financial Time Series Forecasting [83.06074370551887]
  A fixed context window is mismatched to the time-varying optimal look-back of non-stationary price processes.<n>We propose a Mixture-of-Experts framework designed to adaptively determine the temporal context for each input sample.<n> Experiments on cumulative log-return prediction (HS300, S&P500) and fund sales forecasting demonstrate that RAVEN achieves SOTA performances, improves Pearson correlation by 9.2% on HS300 and 20.2% on S&P500, and reduces MSE by 18.2% on fund sales forecasting.
  arXiv  Detail & Related papers  (2026-06-23T02:11:04Z)
• Capacity-Constrained Online Convex Optimization with Delayed Feedback [58.59385794080679]
  We study delayed online convex optimization (OCO) under a hard capacity constraint.<n>We find that for first-order feedback, capacity $C = (log T)$ suffices to recover standard delayed OCO rates up to logarithmic factors.<n>For bandit feedback, the regret rates are modulated by powers of $(1 + _textmax/C)$, where $_textmax$ is the maximum number of pending observations at any time.
  arXiv  Detail & Related papers  (2026-06-10T06:37:06Z)
• Bandit Convex Optimization with Gradient Prediction Adaptivity [56.816177049016794]
  We investigate whether optimistic gradient predictions can improve worst-case regret guarantees in a prediction-adaptive manner.<n>The key idea is a novel variance-reduced gradient estimator whose variance scales with the prediction error rather than the gradient norm.<n>We establish an information-theoretic lower bound that scales as $(sqrtmathbbE[S_T])$, providing a fundamental characterization of the best achievable prediction-adaptive regret.
  arXiv  Detail & Related papers  (2026-05-21T08:57:38Z)
• XGBoost Forecasting of NEPSE Index Log Returns with Walk Forward Validation [1.2347259751353263]
  This study develops a robust machine learning framework for one-step-ahead forecasting of daily log-returns in the Nepal Stock Exchange (NEPSE) Index using the XGBoost regressor.<n>A comprehensive feature set is engineered, including lagged log-returns (up to 30 days) and established technical indicators.<n>Empirical results show that the optimal configuration, an expanding window with 20 lags, outperforms tuned ARIMA and Ridge regression benchmarks.
  arXiv  Detail & Related papers  (2026-01-13T15:22:08Z)
• SGD with Dependent Data: Optimal Estimation, Regret, and Inference [3.038061705362137]
  gradient descent (SGD) is shown to accommodate both independent and dependent information under a broad class of stepsize schedules and exploration rate schemes.<n>We show that SGD simultaneously achieves statistically optimal estimation error and regret, extending and improving existing results.<n>For online sparse regression, we develop a new SGD-based algorithm that uses only $d$ units of storage and requires $O(d)$ flops per iteration.
  arXiv  Detail & Related papers  (2026-01-04T04:52:11Z)
• Contrastive Time Series Forecasting with Anomalies [3.4598718746610593]
  Time series forecasting predicts future values from past data.<n>Some anomalous events have lasting effects and influence the forecast, while others are short-lived and should be ignored.<n>We propose Co-TSFA (Contrastive Time Series Forecasting with Anomalies), a regularization framework that learns when to ignore anomalies and when to respond.
  arXiv  Detail & Related papers  (2025-12-12T12:54:24Z)
• Monitoring State Transitions in Markovian Systems with Sampling Cost [65.4151496405543]
  A natural approach is a greedy policy that predicts when the expected prediction loss is below the query cost and queries otherwise.<n>We analyze this policy in a Markovian setting, where the optimal (OPT) strategy is a state-dependent threshold policy.<n>For the case of unknown transition probabilities, we propose a projected gradient descent (PSGD)-based learning variant of the greedy policy.
  arXiv  Detail & Related papers  (2025-10-25T15:07:37Z)
• Non-Asymptotic Analysis of Efficiency in Conformalized Regression [17.873283539065387]
  We establish non-asymptotic bounds on the deviation of the prediction set length from the oracle interval length for conformalized quantile and median regression trained via SGD.<n>The results identify phase transitions in convergence rates across different regimes of $alpha$, offering guidance for allocating data to control excess prediction set length.
  arXiv  Detail & Related papers  (2025-10-08T14:50:35Z)
• Error-quantified Conformal Inference for Time Series [55.11926160774831]
  Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data.<n>We propose itError-quantified Conformal Inference (ECI) by smoothing the quantile loss function.<n>ECI can achieve valid miscoverage control and output tighter prediction sets than other baselines.
  arXiv  Detail & Related papers  (2025-02-02T15:02:36Z)
• Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling [73.5602474095954]
  We study the non-asymptotic performance of approximation schemes with delayed updates under Markovian sampling. Our theoretical findings shed light on the finite-time effects of delays for a broad class of algorithms.
  arXiv  Detail & Related papers  (2024-02-19T03:08:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.