A semi-supervised interactive algorithm for change point detection

The goal of change point detection (CPD) is to identify abrupt changes in the statistics of signals or time series that reflect transitions in the underlying system’s properties or states. While many statistical and learning-based approaches have been proposed to address this task, most state-of-the-art methods still treat this problem in an unsupervised setting. As a result, there is often a large gap between the algorithm-detected results and the expected outcomes of the user. To bridge this gap, we propose an active-learning strategy for the CPD problem that combines with the one-class support vector machine (OCSVM) model, resulting in an interactive CPD algorithm that improves itself by querying the end-user. This approach enables us to focus on detecting the desired change points and ignore false-positives or irrelevant change points. We demonstrate that the interactive OCSVM model can be combined with various unsupervised CPD models to function in a semi-supervised setting, resulting in improved detection accuracy. Our experimental results on various simulated and real-life datasets demonstrate a significant improvement in detection performance on both single- and multi-channel time series, even with a limited number of queries.