Latent Neural Stochastic Differential Equations for Change Point Detection

• URL: http://arxiv.org/abs/2208.10317v2
• Date: Wed, 4 Oct 2023 10:47:58 GMT
• Title: Latent Neural Stochastic Differential Equations for Change Point Detection
• Authors: Artem Ryzhikov, Mikhail Hushchyn and Denis Derkach
• Abstract summary: We present a novel change point detection algorithm based on Latent Neural 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 processes in different timestamps to find change points of the process.
• Score: 0.6445605125467574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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