Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation. In this work, we propose a novel type of normalizing flow driven by a differential deformation of the continuous-time Wiener process. As a result, we obtain a rich time series model whose observable process inherits many of the appealing properties of its base process, such as efficient computation of likelihoods and marginals. Furthermore, our continuous treatment provides a natural framework for irregular time series with an independent arrival process, including straightforward interpolation. We illustrate the desirable properties of the proposed model on popular stochastic processes and demonstrate its superior flexibility to variational RNN and latent ODE baselines in a series of experiments on synthetic and real-world data.

本文提出一种新型的正则化流，其基于 Wiener 过程的微分变形，从而获得一个完整的时间序列模型，该模型继承了其基本过程的许多性质，如似然性和边缘效率。此外，我们的连续处理为有独立到达过程的不规则时间序列提供了自然框架，包括直接插值。在合成数据和真实世界数据的一系列实验中，我们证明了该模型相对于变分 RNN 和潜在 ODE 基线的优越灵活性。

使用动态规范流建模连续随机过程