Latent Neural Stochastic Differential Equations for Change Point Detection

TL;DR

This paper introduces a novel change point detection method using Latent Neural Stochastic Differential Equations, which models complex system dynamics in a learned latent space and outperforms existing algorithms on various datasets.

Contribution

The paper presents a new change point detection algorithm based on neural SDEs that learns a latent representation and estimates process evolution for improved detection accuracy.

Findings

Outperforms state-of-the-art algorithms on synthetic datasets

Effective in real-world change point detection scenarios

Models complex dynamics with neural stochastic differential equations

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.