论文标题
用于更改点检测的在线神经网络
Online Neural Networks for Change-Point Detection
论文作者
论文摘要
时间序列改变其行为时的时刻称为变更点。检测到这一点是一个众所周知的问题，可以在许多应用中找到：工业过程的质量监测，复杂系统中的故障检测，健康监测，语音识别和视频分析。变化点的发生意味着系统状态已改变，其及时检测可能有助于防止不必要的后果。在本文中，我们提出了两种基于神经网络的在线更改点检测方法。这些算法表明线性计算复杂性，适合在大时间序列中的更改点检测。我们将它们与各种合成和现实世界数据集的最著名算法进行了比较。实验表明，所提出的方法优于已知方法。
Moments when a time series changes its behaviour are called change points. Detection of such points is a well-known problem, which can be found in many applications: quality monitoring of industrial processes, failure detection in complex systems, health monitoring, speech recognition and video analysis. 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 online change-point detection approaches based on neural networks. 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.
