[2502.05709] Flow-based Conformal Prediction for Multi-dimensional Time Series

Computer Science > Machine Learning arXiv:2502.05709 (cs) [Submitted on 8 Feb 2025 (v1), last revised 20 Mar 2026 (this version, v3)] Title:Flow-based Conformal Prediction for Multi-dimensional Time Series Authors:Junghwan Lee, Chen Xu, Yao Xie View a PDF of the paper titled Flow-based Conformal Prediction for Multi-dimensional Time Series, by Junghwan Lee and 2 other authors View PDF HTML (experimental) Abstract:Time series prediction underpins a broad range of downstream tasks across many scientific domains. Recent advances and increasing adoption of black-box machine learning models for time series prediction highlight the critical need for uncertainty quantification. While conformal prediction has gained attention as a reliable uncertainty quantification method, conformal prediction for time series faces two key challenges: (1) \textbf{leveraging correlations in observations and non-conformity scores to overcome the exchangeability assumption}, and (2) \textbf{constructing prediction sets for multi-dimensional outcomes}. To address these challenges, we propose a novel conformal prediction method for time series using flow with classifier-free guidance. We provide coverage guarantees by establishing exact non-asymptotic marginal coverage and a finite-sample bound on conditional coverage for the proposed method. Evaluations on real-world time series datasets demonstrate that our method constructs significantly smaller prediction sets than existing conformal prediction me...