Conformalized Adaptive Forecasting of Heterogeneous Trajectories
Yanfei Zhou Lars Lindemann Matteo Sesia
This paper presents a new conformal method for generating simultaneous
forecasting bands guaranteed to cover the entire path of a new random
trajectory with sufficiently high probability. Prompted by the need for
dependable uncertainty estimates in motion planning applications where the
behavior of diverse objects may be more or less unpredictable, we blend
different techniques from online conformal prediction of single and multiple
time series, as well as ideas for addressing heteroscedasticity in regression.
This solution is both principled, providing precise finite-sample guarantees,
and effective, often leading to more informative predictions than prior
methods.