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PathFM: Fast Generative Forecasting of Multivariate Trajectories

Paper recorded by Signals 4 on 2026-10-15 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16

Category: cs.LG · 机器学习 · first seen 2026-10-16

Abstract

Time series foundation models are becoming increasingly popular for general-purpose forecasting. However, most leading models predict marginal quantiles, leaving the dependence structure of future trajectories unspecified. This distinction matters for trajectory-dependent quantities such as cumulative demand, peak load, and event timing, yet point and marginal scores do not reveal it. To model thi

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#266 most recent of 749 cs.LG papers we have recorded · ↑ newer: Escaping Parameter Space: Tight Generalization Bounds via Representati · ↓ older: Lean Graph Search for Formal Theorem Proving
Cite this page: PathFM: Fast Generative Forecasting of Multivariate Trajectories: the #266 most recent of 749 cs.LG papers we have recorded (as of 2026-10-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/pathfm-fast-generative-forecasting-of-multivariate-trajectories.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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