Conformal Prediction for Time-series Forecasting with Change Points

• URL: http://arxiv.org/abs/2509.02844v3
• Date: Thu, 23 Oct 2025 07:33:29 GMT
• Title: Conformal Prediction for Time-series Forecasting with Change Points
• Authors: Sophia Sun, Rose Yu,
• Abstract summary: We propose a novel Conformal Prediction for Time-series with Change points (CPTC) algorithm.<n>We prove CPTC's validity and improved adaptivity in the time series setting under minimum assumptions.
• Score: 26.947702126448203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conformal prediction has been explored as a general and efficient way to provide uncertainty quantification for time series. However, current methods struggle to handle time series data with change points - sudden shifts in the underlying data-generating process. In this paper, we propose a novel Conformal Prediction for Time-series with Change points (CPTC) algorithm, addressing this gap by integrating a model to predict the underlying state with online conformal prediction to model uncertainties in non-stationary time series. We prove CPTC's validity and improved adaptivity in the time series setting under minimum assumptions, and demonstrate CPTC's practical effectiveness on 6 synthetic and real-world datasets, showing improved validity and adaptivity compared to state-of-the-art baselines.

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