  Transient recurring phenomena are ubiquitous in many scientific fields like
neuroscience and meteorology. Time inhomogenous Vector Autoregressive Models
(VAR) may be used to characterize peri-event system dynamics associated with
such phenomena, and can be learned by exploiting multi-dimensional data
gathering samples of the evolution of the system in multiple time windows
comprising, each associated with one occurrence of the transient phenomenon,
that we will call "trial". However, optimal VAR model order selection methods,
commonly relying on the Akaike or Bayesian Information Criteria (AIC/BIC), are
typically not designed for multi-trial data. Here we derive the BIC methods for
multi-trial ensemble data which are gathered after the detection of the events.
We show using simulated bivariate AR models that the multi-trial BIC is able to
recover the real model order. We also demonstrate with simulated transient
events and real data that the multi-trial BIC is able to estimate a
sufficiently small model order for dynamic system modeling.
