Disentangling time constant and time dependent hidden state in time series with variational Bayesian inference

Abstract

In this thesis, we design and explore a new model architecture called a Variational Bayes Recurrent Neural Network (VBRNN) for modelling time series. The VBRNN contains explicit structure to disentangle time constant and time dependent dynamics for use with compatible time series, such as those that can be modelled by differential equations with time constant parameters and time dependent state. The model consists of a Variational Bayes (VB) layer to infer time constant state, as well as a conditioned-RNN to model time dependent dynamics. The VBRNN is explored through various synthetic datasets and problems, and compared to conventional methods on these datasets. This approach demonstrates effective disentanglement, motivating future work to explore the efficacy of this mo del in real word datasets.