Kavli Affiliate: Eric Miller
| First 5 Authors: Michael T. Wojnowicz, Michael T. Wojnowicz, , ,
| Summary:
We seek a computationally efficient model for a collection of time series
arising from multiple interacting entities (a.k.a. "agents"). Recent models of
temporal patterns across individuals fail to incorporate explicit system-level
collective behavior that can influence the trajectories of individual entities.
To address this gap in the literature, we present a new hierarchical
switching-state model that can be trained in an unsupervised fashion to
simultaneously learn both system-level and individual-level dynamics. We employ
a latent system-level discrete state Markov chain that provides top-down
influence on latent entity-level chains which in turn govern the emission of
each observed time series. Recurrent feedback from the observations to the
latent chains at both entity and system levels allows recent situational
context to inform how dynamics unfold at all levels in bottom-up fashion. We
hypothesize that including both top-down and bottom-up influences on group
dynamics will improve interpretability of the learned dynamics and reduce error
when forecasting. Our hierarchical switching recurrent dynamical model can be
learned via closed-form variational coordinate ascent updates to all latent
chains that scale linearly in the number of entities. This is asymptotically no
more costly than fitting a separate model for each entity. Analysis of both
synthetic data and real basketball team movements suggests our lean parametric
model can achieve competitive forecasts compared to larger neural network
models that require far more computational resources. Further experiments on
soldier data as well as a synthetic task with 64 cooperating entities show how
our approach can yield interpretable insights about team dynamics over time.
| Search Query: ArXiv Query: search_query=au:”Eric Miller”&id_list=&start=0&max_results=3