Bayesian Time Series Structure Learning: Formulation of an Event Driven Prior Distribution

We study the prior distribution over structures of a Bayesian time series structure learning model—the Temporal Interaction Model (TIM) of Siracusa and Fisher III. We develop a new method for setting the hyperparameters of the TIM structure prior. Our contribution enables more consistent inference performance as the number of interacting nodes in the time series increases, which we show analytically and with synthetic experiments. Secondly, we prove that the form of the prior distribution is within the curved exponential family. Finally, we test our developments empirically. Because traffic dynamics are comparatively accessible to common knowledge, we choose traffic time series as a test case to examine general behaviors of TIM inference and in particular our parameterization of the structure prior.

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

2023

Author dc:creator

• Forman, David J.

Advisor dc:contributor.advisor

• Fisher III, John W.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151224

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151224