  Many complex time series can be effectively subdivided into distinct regimes
that exhibit persistent dynamics. Discovering the switching behavior and the
statistical patterns in these regimes is important for understanding the
underlying dynamical system. We propose the Recurrent Explicit Duration
Switching Dynamical System (RED-SDS), a flexible model that is capable of
identifying both state- and time-dependent switching dynamics. State-dependent
switching is enabled by a recurrent state-to-switch connection and an explicit
duration count variable is used to improve the time-dependent switching
behavior. We demonstrate how to perform efficient inference using a hybrid
algorithm that approximates the posterior of the continuous states via an
inference network and performs exact inference for the discrete switches and
counts. The model is trained by maximizing a Monte Carlo lower bound of the
marginal log-likelihood that can be computed efficiently as a byproduct of the
inference routine. Empirical results on multiple datasets demonstrate that
RED-SDS achieves considerable improvement in time series segmentation and
competitive forecasting performance against the state of the art.
