Conformalized Adaptive Forecasting of Heterogeneous Trajectories

Yanfei Zhou, Lars Lindemann, Matteo Sesia
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:62002-62056, 2024.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-zhou24l, title = {Conformalized Adaptive Forecasting of Heterogeneous Trajectories}, author = {Zhou, Yanfei and Lindemann, Lars and Sesia, Matteo}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {62002--62056}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhou24l/zhou24l.pdf}, url = {https://proceedings.mlr.press/v235/zhou24l.html}, 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.} }
Endnote
%0 Conference Paper %T Conformalized Adaptive Forecasting of Heterogeneous Trajectories %A Yanfei Zhou %A Lars Lindemann %A Matteo Sesia %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-zhou24l %I PMLR %P 62002--62056 %U https://proceedings.mlr.press/v235/zhou24l.html %V 235 %X 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.
APA
Zhou, Y., Lindemann, L. & Sesia, M.. (2024). Conformalized Adaptive Forecasting of Heterogeneous Trajectories. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:62002-62056 Available from https://proceedings.mlr.press/v235/zhou24l.html.

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