Moments when a time series changes its behavior are called change points. Occurrence of change point implies that the state of the system is altered and its timely detection might help to prevent unwanted consequences. In this paper, we present two change-point detection approaches based on neural networks and online learning. These algorithms demonstrate linear computational complexity and are suitable for change-point detection in large time series. We compare them with the best known algorithms on various synthetic and real world data sets. Experiments show that the proposed methods outperform known approaches. We also prove the convergence of the algorithms to the optimal solutions and describe conditions rendering current approach more powerful than offline one.

翻译：时间序列行为发生变化的时刻称为变化点。变化点的出现意味着系统状态发生改变，及时检测变化点有助于避免不良后果。本文提出了两种基于神经网络和在线学习的变化点检测方法。这些算法具有线性计算复杂度，适用于大规模时间序列中的变化点检测。我们在多种合成数据集和真实数据集上将其与最知名的算法进行比较。实验表明，所提出的方法优于已知方法。我们还证明了算法向最优解的收敛性，并阐明了当前方法优于离线方法的适用条件。