Conformalized Adaptive Forecasting of Heterogeneous Trajectories

• URL: http://arxiv.org/abs/2402.09623v2
• Date: Wed, 15 May 2024 04:38:46 GMT
• Title: Conformalized Adaptive Forecasting of Heterogeneous Trajectories
• Authors: Yanfei Zhou, Lars Lindemann, Matteo Sesia,
• Abstract summary: We present a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high probability. This solution is both principled, providing precise finite-sample guarantees, and effective, often leading to more informative predictions than prior methods.
• Score: 8.022222226139032
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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