Identifying Latent Stochastic Differential Equations

Journal Article (Journal Article)

We present a method for learning latent stochastic differential equations (SDEs) from high dimensional time series data. Given a high-dimensional time series generated from a lower dimensional latent unknown Itô process, the proposed method learns the mapping from ambient to latent space, and the underlying SDE coefficients, through a self-supervised learning approach. Using the framework of variational autoencoders, we consider a conditional generative model for the data based on the Euler-Maruyama approximation of SDE solutions. Furthermore, we use recent results on identifiability of latent variable models to show that the proposed model can recover not only the underlying SDE coefficients, but also the original latent variables, up to an isometry, in the limit of infinite data. We validate the method through several simulated video processing tasks, where the underlying SDE is known, and through real world datasets.

Full Text

Duke Authors

Cited Authors

  • Hasan, A; Pereira, JM; Farsiu, S; Tarokh, V

Published Date

  • January 1, 2022

Published In

Volume / Issue

  • 70 /

Start / End Page

  • 89 - 104

Electronic International Standard Serial Number (EISSN)

  • 1941-0476

International Standard Serial Number (ISSN)

  • 1053-587X

Digital Object Identifier (DOI)

  • 10.1109/TSP.2021.3131723

Citation Source

  • Scopus