  The accurate and interpretable prediction of future events in time-series
data often requires the capturing of representative patterns (or referred to as
states) underpinning the observed data. To this end, most existing studies
focus on the representation and recognition of states, but ignore the changing
transitional relations among them. In this paper, we present evolutionary state
graph, a dynamic graph structure designed to systematically represent the
evolving relations (edges) among states (nodes) along time. We conduct analysis
on the dynamic graphs constructed from the time-series data and show that
changes on the graph structures (e.g., edges connecting certain state nodes)
can inform the occurrences of events (i.e., time-series fluctuation). Inspired
by this, we propose a novel graph neural network model, Evolutionary State
Graph Network (EvoNet), to encode the evolutionary state graph for accurate and
interpretable time-series event prediction. Specifically, Evolutionary State
Graph Network models both the node-level (state-to-state) and graph-level
(segment-to-segment) propagation, and captures the node-graph
(state-to-segment) interactions over time. Experimental results based on five
real-world datasets show that our approach not only achieves clear improvements
compared with 11 baselines, but also provides more insights towards explaining
the results of event predictions.

Machine Learning;Machine Learning