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 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.

翻译：在这项工作中,我们提出了一种由Wiener进程不同变形驱动的新型正常流动模式。结果,我们获得了一个丰富的时间序列模型,其可观察过程继承了其基础过程许多具有吸引力的特性,例如有效计算可能性和边缘值。此外,我们的连续处理为不规则的时间序列提供了一个自然框架,有独立的到达过程,包括直截了当的内插。我们展示了拟议的大众切换过程模型的可取性,并展示了该模型在合成和现实世界数据的一系列实验中相对于变异的 RNN 和潜伏 ODE基线的高度灵活性。