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.
Forty-first International Conference on Machine Learning , year=
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PIMSM is a Mamba-based architecture that maps knee frequencies from spectra to multi-scale discretization parameters to reduce representation drift under distribution shifts in fMRI and weather forecasting.
TabPFN-TS captures simple target-covariate relationships more effectively than Chronos-2 in controlled experiments, especially for short horizons.
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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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PIMSM: Physics-Informed Multi-Scale Mamba for Stable Neural Representations under Distribution Shift
PIMSM is a Mamba-based architecture that maps knee frequencies from spectra to multi-scale discretization parameters to reduce representation drift under distribution shifts in fMRI and weather forecasting.
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Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS
TabPFN-TS captures simple target-covariate relationships more effectively than Chronos-2 in controlled experiments, especially for short horizons.