Spatio-temporal graphical models for extreme events

We propose a novel statistical model to describe spatio-temporal extreme events. The model can be used to estimate extreme-value temporal pattern such as seasonality and trend, and further to predict the distribution of extreme events in the future. The basic idea is to explore graphical models to capture the highly structured dependencies among extreme events measured in time and space. More explicitly, we first assume the single observation at each location and time point follows a Generalized Extreme Value (GEV) distribution. The spatio-temporal dependencies are further encoded via graphical models imposed on the GEV parameters. We develop efficient learning and inference algorithms for the resulting non-Gaussian graphical model. Results of both synthetic and real data demonstrate the effectiveness of the proposed approach.