STEPS reformulates test-time adaptation for time series forecasting as a Dirichlet boundary value problem on a temporal manifold and solves for smooth error corrections, yielding 26.82% average relative MSE reduction over zero-shot baselines.
Proceedings of the AAAI Conference on Artificial Intelligence , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
AeroSense predicts regional air traffic flow from instantaneous aircraft states rather than historical time-series aggregates, showing accuracy gains especially in dense traffic.
citing papers explorer
-
STEPS: A Temporal Smooth Error Propagation Solver on the Manifolds for Test-Time Adaptation in Time Series Forecasting
STEPS reformulates test-time adaptation for time series forecasting as a Dirichlet boundary value problem on a temporal manifold and solves for smooth error corrections, yielding 26.82% average relative MSE reduction over zero-shot baselines.
-
Unlocking air traffic flow prediction through microscopic aircraft-state modeling
AeroSense predicts regional air traffic flow from instantaneous aircraft states rather than historical time-series aggregates, showing accuracy gains especially in dense traffic.