  In this paper we introduce a novel algorithm called Iterative Section
Reduction (ISR) to automatically identify sub-intervals of spatiotemporal time
series that are predictive of a target classification task. Specifically, using
data collected from a driving simulator study, we identify which spatial
regions (dubbed "sections") along the simulated routes tend to manifest driving
behaviors that are predictive of the presence of Attention Deficit
Hyperactivity Disorder (ADHD). Identifying these sections is important for two
main reasons: (1) to improve predictive accuracy of the trained models by
filtering out non-predictive time series sub-intervals, and (2) to gain
insights into which on-road scenarios (dubbed events) elicit distinctly
different driving behaviors from patients undergoing treatment for ADHD versus
those that are not. Our experimental results show both improved performance
over prior efforts (+10% accuracy) and good alignment between the predictive
sections identified and scripted on-road events in the simulator (negotiating
turns and curves).
