让用户参与反馈，让多分辨率变化点检测更准更懂人
MuRAL-CPD: Active Learning for Multiresolution Change Point Detection
- 用小波分解在多时间尺度检测变化点，结合用户反馈优化参数
- 在少量标注下性能超越现有方法，提升检测准确率与可解释性
- 适合需要人工干预、数据标注少的工业监测与金融分析场景
变化点检测（CPD）是时间序列分析中的关键任务，旨在识别数据生成过程发生转变的时刻。传统方法多依赖无监督技术，难以适配特定任务对变化的定义，也无法利用用户知识。为此，我们提出MuRAL-CPD，一种将主动学习融入多分辨率CPD算法的新型半监督方法。该方法采用小波基多分辨率分解，在多个时间尺度上检测变化，并通过用户反馈迭代优化关键超参数。这一交互机制使模型对变化的理解与用户一致，从而提升准确率与可解释性。在多个真实世界数据集上的实验表明，该方法在仅有少量监督的情况下，显著优于现有先进方法。
原文摘要 · Abstract (English)
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.
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