  We introduce deep switching auto-regressive factorization (DSARF), a deep
generative model for spatio-temporal data with the capability to unravel
recurring patterns in the data and perform robust short- and long-term
predictions. Similar to other factor analysis methods, DSARF approximates high
dimensional data by a product between time dependent weights and spatially
dependent factors. These weights and factors are in turn represented in terms
of lower dimensional latent variables that are inferred using stochastic
variational inference. DSARF is different from the state-of-the-art techniques
in that it parameterizes the weights in terms of a deep switching vector
auto-regressive likelihood governed with a Markovian prior, which is able to
capture the non-linear inter-dependencies among weights to characterize
multimodal temporal dynamics. This results in a flexible hierarchical deep
generative factor analysis model that can be extended to (i) provide a
collection of potentially interpretable states abstracted from the process
dynamics, and (ii) perform short- and long-term vector time series prediction
in a complex multi-relational setting. Our extensive experiments, which include
simulated data and real data from a wide range of applications such as climate
change, weather forecasting, traffic, infectious disease spread and nonlinear
physical systems attest the superior performance of DSARF in terms of long- and
short-term prediction error, when compared with the state-of-the-art methods.
