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=
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AeroSense learns an end-to-end mapping from instantaneous aircraft-state situations to future regional traffic flow and reports higher accuracy than aggregation-based baselines on real data, especially in high-density conditions.
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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.
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Unlocking air traffic flow prediction through microscopic aircraft-state modeling
AeroSense learns an end-to-end mapping from instantaneous aircraft-state situations to future regional traffic flow and reports higher accuracy than aggregation-based baselines on real data, especially in high-density conditions.