FLIPR: FLexible and Interpretable Prediction Regions for time series

Eshant English, Christoph Lippert
Conference on Parsimony and Learning, PMLR 328:1101-1111, 2026.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v328-english26a, title = {FLIPR: FLexible and Interpretable Prediction Regions for time series}, author = {English, Eshant and Lippert, Christoph}, booktitle = {Conference on Parsimony and Learning}, pages = {1101--1111}, year = {2026}, editor = {Burkholz, Rebekka and Liu, Shiwei and Ravishankar, Saiprasad and Redman, William and Huang, Wei and Su, Weijie and Zhu, Zhihui}, volume = {328}, series = {Proceedings of Machine Learning Research}, month = {23--26 Mar}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v328/main/assets/english26a/english26a.pdf}, url = {https://proceedings.mlr.press/v328/english26a.html}, 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.} }
Endnote
%0 Conference Paper %T FLIPR: FLexible and Interpretable Prediction Regions for time series %A Eshant English %A Christoph Lippert %B Conference on Parsimony and Learning %C Proceedings of Machine Learning Research %D 2026 %E Rebekka Burkholz %E Shiwei Liu %E Saiprasad Ravishankar %E William Redman %E Wei Huang %E Weijie Su %E Zhihui Zhu %F pmlr-v328-english26a %I PMLR %P 1101--1111 %U https://proceedings.mlr.press/v328/english26a.html %V 328 %X 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.
APA
English, E. & Lippert, C.. (2026). FLIPR: FLexible and Interpretable Prediction Regions for time series. Conference on Parsimony and Learning, in Proceedings of Machine Learning Research 328:1101-1111 Available from https://proceedings.mlr.press/v328/english26a.html.

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