Conformal Prediction for Time Series with Deep Sequence Models
Abstract
Recent advances in deep learning for time series prediction have amplified the need for reliable uncertainty quantification. Conformal prediction has gained attention as a distribution-free framework for constructing prediction intervals with coverage guarantees. However, its coverage guarantees rely on data exchangeability, an assumption generally violated in time series. Active research has focused on developing conformal prediction methods for time series that overcome this limitation. While deep sequence models, such as recurrent neural networks and Transformers, have often been used in conformal prediction for time series, limited work has systematically studied how deep sequence models can be utilized in conformal prediction for time series. In this work, we systematically investigate the use of deep sequence models in conformal prediction for time series through three approaches: conditional quantile regression, conditional quantile function estimation, and localized conformal prediction. We provide a theoretical analysis establishing asymptotic conditional coverage guarantees for all three approaches under suitable assumptions. Through comprehensive experiments on real-world datasets, we demonstrate the effectiveness of leveraging deep sequence models into conformal prediction for time series.
Related
- Efficient conformal prediction intervals for time series: Online PID-Expert aggregation
- conformalForecast: An R package for sequential conformal prediction in time series
- State-Matched Sequence Conformal Prediction
- Dynamic Dual-Feedback Conformal Inference for Time Series Forecasting
- Conformal Coverage of Time Series: Validity and Inference
BibTeX
@misc{lee2026conformal,
title = {{Conformal Prediction for Time Series with Deep Sequence Models}},
author = {Junghwan Lee and Jonghyeok Lee and Yao Xie},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.02357},
eprint = {2610.02357},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.02357},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Conformal Prediction for Time Series with Deep Sequence Models
AU - Junghwan Lee
AU - Jonghyeok Lee
AU - Yao Xie
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.02357
UR - https://arxiv.org/abs/2610.02357
AB - Recent advances in deep learning for time series prediction have amplified the need for reliable uncertainty quantification. Conformal prediction has gained attention as a distribution-free framework for constructing prediction intervals with coverage guarantees. However, its coverage guarantees rely on data exchangeability, an assumption generally violated in time series. Active research has focused on developing conformal prediction methods for time series that overcome this limitation. While deep sequence models, such as recurrent neural networks and Transformers, have often been used in conformal prediction for time series, limited work has systematically studied how deep sequence models can be utilized in conformal prediction for time series. In this work, we systematically investigate the use of deep sequence models in conformal prediction for time series through three approaches: conditional quantile regression, conditional quantile function estimation, and localized conformal prediction. We provide a theoretical analysis establishing asymptotic conditional coverage guarantees for all three approaches under suitable assumptions. Through comprehensive experiments on real-world datasets, we demonstrate the effectiveness of leveraging deep sequence models into conformal prediction for time series.
ER -