Offline changepoint localization using a matrix of conformal p-values

Sanjit Dandapanthula; Aaditya Ramdas

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arXiv · 2505.00292

Offline changepoint localization using a matrix of conformal p-values

Sanjit Dandapanthula·Aaditya Ramdas

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Abstract

Changepoint localization is the problem of estimating the index at which a change occurred in the data generating distribution of an ordered list of data, or declaring that no change occurred. We present the broadly applicable MCP algorithm, which uses a matrix of conformal p-values to produce a confidence interval for a (single) changepoint under the mild assumption that the pre-change and post-change distributions are each exchangeable. We prove a novel conformal Neyman-Pearson lemma, motivating practical classifier-based choices for our conformal score function. Finally, we exemplify the MCP algorithm on a variety of synthetic and real-world datasets, including using black-box pre-trained classifiers to detect changes in sequences of images, text, and accelerometer data.

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Sanjit Dandapanthula, Aaditya Ramdas. 2025-05-01. Offline changepoint localization using a matrix of conformal p-values. https://arxiv.org/abs/2505.00292

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