MuRAL-CPD: Active Learning for Multiresolution Change Point Detection

TL;DR

MuRAL-CPD introduces a semi-supervised, active learning approach for multiresolution change point detection in time series, enhancing adaptability, accuracy, and interpretability with minimal supervision.

Contribution

It presents MuRAL-CPD, a novel method combining wavelet-based multiresolution analysis with active learning to improve change point detection.

Findings

Outperforms state-of-the-art methods on real-world datasets

Effective with minimal supervision

Improves interpretability of change points

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.