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.

翻译：变点检测（CPD）是时间序列分析中的一项关键任务，旨在识别底层数据生成过程发生转变的时刻。传统的CPD方法通常依赖于无监督技术，这些方法缺乏对任务特定变化定义的适应性，且无法利用用户知识。为应对这些局限，我们提出了MuRAL-CPD，一种新颖的半监督方法，它将主动学习集成到多分辨率CPD算法中。MuRAL-CPD利用基于小波的多分辨率分解来检测跨多个时间尺度的变化，并结合用户反馈以迭代方式优化关键超参数。这种交互使模型能够将其对变化的定义与用户的认知对齐，从而提升准确性和可解释性。我们在多个真实数据集上的实验结果表明，MuRAL-CPD相较于现有先进方法具有显著优势，特别是在仅有少量监督可用的场景中。