  Spatio-temporal forecasting has numerous applications in analyzing wireless,
traffic, and financial networks. Many classical statistical models often fall
short in handling the complexity and high non-linearity present in time-series
data. Recent advances in deep learning allow for better modelling of spatial
and temporal dependencies. While most of these models focus on obtaining
accurate point forecasts, they do not characterize the prediction uncertainty.
In this work, we consider the time-series data as a random realization from a
nonlinear state-space model and target Bayesian inference of the hidden states
for probabilistic forecasting. We use particle flow as the tool for
approximating the posterior distribution of the states, as it is shown to be
highly effective in complex, high-dimensional settings. Thorough
experimentation on several real world time-series datasets demonstrates that
our approach provides better characterization of uncertainty while maintaining
comparable accuracy to the state-of-the art point forecasting methods.
