Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics. We can gain insight into these systems by decomposing the data into segments that are each explained by simpler dynamic units. Building on switching linear dynamical systems (SLDS), we present a new model class that not only discovers these dynamical units, but also explains how their switching behavior depends on observations or continuous latent states. These "recurrent" switching linear dynamical systems provide further insight by discovering the conditions under which each unit is deployed, something that traditional SLDS models fail to do. We leverage recent algorithmic advances in approximate inference to make Bayesian inference in these models easy, fast, and scalable.
@conference{linderman2017recurrent,
year = {2017},
author = {Linderman, Scott W. and Johnson, Matthew J. and Miller, Andrew C. and Adams, Ryan P. and Blei, David M. and Paninski, Liam},
title = {Recurrent Switching Linear Dynamical Systems},
booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS)},
keywords = {AISTATS, graphical models},
note = {arXiv:1610.08466 [stat.ML]}
}