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

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Grullon, Dylan Emanuel Centeno.

Advisor dc:contributor.advisor

• Duane S. Boning.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124572

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124572