MuRAL-CPD: Active Learning for Multiresolution Change Point Detection
Abstract
Change Point Detection (CPD) is a critical task in time series analysis, aiming to identify moments when the underlying data-generating process shifts. Traditional CPD methods often rely on unsupervised techniques, which lack adaptability to task-specific definitions of change and cannot benefit from user knowledge. To address these limitations, we propose MuRAL-CPD, a novel semi-supervised method that integrates active learning into a multiresolution CPD algorithm. MuRAL-CPD leverages a wavelet-based multiresolution decomposition to detect changes across multiple temporal scales and incorporates user feedback to iteratively optimize key hyperparameters. This interaction enables the model to align its notion of change with that of the user, improving both accuracy and interpretability. Our experimental results on several real-world datasets show the effectiveness of MuRAL-CPD against state-of-the-art methods, particularly in scenarios where minimal supervision is available.
Cite
@article{arxiv.2601.20686,
title = {MuRAL-CPD: Active Learning for Multiresolution Change Point Detection},
author = {Stefano Bertolasi and Diego Carrera and Diego Stucchi and Pasqualina Fragneto and Luigi Amedeo Bianchi},
journal= {arXiv preprint arXiv:2601.20686},
year = {2026}
}
Comments
Presented at 2025 IEEE International Conference on Data Mining (ICDM), to appear in the Proceedings