Standard online change point detection (CPD) methods tend to have large false discovery rates as their detections are sensitive to outliers. To overcome this drawback, we propose Greedy Online Change Point Detection (GOCPD), a computationally appealing method which finds change points by maximizing the probability of the data coming from the (temporal) concatenation of two independent models. We show that, for time series with a single change point, this objective is unimodal and thus CPD can be accelerated via ternary search with logarithmic complexity. We demonstrate the effectiveness of GOCPD on synthetic data and validate our findings on real-world univariate and multivariate settings.

翻译：标准在线变点检测方法通常因对异常值敏感而导致较高的假发现率。为克服这一缺陷，我们提出贪婪在线变点检测（GOCPD），该计算高效方法通过最大化数据来自两个独立模型（按时间顺序）拼接的概率来定位变点。我们证明，对于仅含单个变点的时间序列，该目标函数具有单峰性，从而可通过三分搜索实现对数复杂度的加速检测。我们在合成数据上验证了GOCPD的有效性，并在一维与多维实际场景中进一步证实了该方法的可靠性。