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Title: Conformalized Adaptive Forecasting of Heterogeneous Trajectories
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.  more » « less
Award ID(s):
2210637
PAR ID:
10625831
Author(s) / Creator(s):
; ;
Publisher / Repository:
PMLR
Date Published:
Format(s):
Medium: X
Location:
Proceedings of the 41st International Conference on Machine Learning
Sponsoring Org:
National Science Foundation
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