Latent Neural Stochastic Differential Equations for Change Point Detection

by   Artem Ryzhikov, et al.

The purpose of change point detection algorithms is to locate an abrupt change in the time evolution of a process. In this paper, we introduce an application of latent neural stochastic differential equations for change point detection problem. We demonstrate the detection capabilities and performance of our model on a range of synthetic and real-world datasets and benchmarks. Most of the studied scenarios show that the proposed algorithm outperforms the state-of-the-art algorithms. We also discuss the strengths and limitations of this approach and indicate directions for further improvements.


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