RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting

Soumyasundar Pal, Liheng Ma, Yingxue Zhang, Mark Coates

Abstract:

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-theart point forecasting methods.