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.
Applied and Computational Harmonic Analysis , volume=
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Proves sharp operator-norm concentration and expectation bounds for sample cross-covariances of sub-Gaussian and Gaussian vectors, governed by effective ranks of the marginal covariances.
A logarithmic centroid method recovers adiabatic Kramers scaling for coherence resonance in a quiescent SRK model and reveals a noise-driven transition to functional synchronization in gap-junction coupled systems.
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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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Concentration Inequalities for Sample Cross-Covariances
Proves sharp operator-norm concentration and expectation bounds for sample cross-covariances of sub-Gaussian and Gaussian vectors, governed by effective ranks of the marginal covariances.
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Breakdown of Adiabatic Scaling and Noise-Induced Functional Synchronization in Deeply Quiescent Excitable Systems
A logarithmic centroid method recovers adiabatic Kramers scaling for coherence resonance in a quiescent SRK model and reveals a noise-driven transition to functional synchronization in gap-junction coupled systems.