Latent Neural Stochastic Differential Equations for Change Point Detection
This work addresses change point detection for analyzing complex systems with multiple readouts, representing an incremental improvement over existing algorithms.
The authors tackled the problem of automated change point detection in complex systems by proposing a novel algorithm based on Latent Neural Stochastic Differential Equations, which outperformed state-of-the-art methods in most experiments.
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.