论文标题
潜在神经随机微分方程变化点检测
Latent Neural Stochastic Differential Equations for Change Point Detection
论文作者
论文摘要
基于多个读数的复杂系统的自动分析仍然是一个挑战。更改点检测算法的目的是在过程的时间序列行为中找到突然的变化。在本文中，我们提出了一种基于潜在神经随机微分方程（SDE）的新型变更点检测算法。我们的方法学习了该过程向潜在空间的非线性深度学习转变，并估计了一个SDE，该SDE描述了其随着时间的流逝。该算法使用不同时间戳中学习的随机过程的似然比来找到该过程的变化点。我们演示了我们在合成和现实世界数据集上算法的检测功能和性能。所提出的方法优于我们大多数实验的最新算法。
Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linear deep learning transformation of the process into a latent space and estimates a SDE that describes its evolution over time. The algorithm uses the likelihood ratio of the learned stochastic processes in different timestamps to find change points of the process. We demonstrate the detection capabilities and performance of our algorithm on synthetic and real-world datasets. The proposed method outperforms the state-of-the-art algorithms on the majority of our experiments.
