FLIPR: FLexible and Interpretable Prediction Regions for time series

Eshant English, Christoph Lippert

Keywords: interpretable regions, time series, conformal prediction

TL;DR: A new set of conformity scores that enhance interpretability of the prediction regions with balance

Abstract: Constructing reliable and interpretable prediction regions remains a core challenge in time-series forecasting. While conformal prediction offers rigorous finite-sample coverage guarantees, most existing approaches focus on univariate intervals and fail to capture dependencies across multiple forecast horizons. We propose FLexible and Interpretable Prediction Regions (FLIPR) for time series, a flexible and interpretable conformal framework that constructs balanced joint prediction regions for multi-horizon forecasts. FLIPR for time series produces a $K-$th–order conformity score that jointly calibrates horizon-wise residuals using standardised mean and scale estimates, enabling explicit control of $K-$family-wise error while preserving interpretability. The resulting regions are rectangular yet adaptive, distributing coverage uniformly across horizons without requiring any additional learned model. Empirical results on synthetic and real-world datasets show that FLIPR achieves valid coverage with compact, well-calibrated prediction regions, outperforming existing conformal baselines in efficiency and interpretability.

Submission Number: 105