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 26th ACM SIGKDD international conference on knowledge discovery & data mining , pages=
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LTE-ODE repurposes local truncation error as an unsupervised dynamic attention mask that preserves continuous Neural ODE evolution in stable regions while triggering discrete compensation only at anomaly points in large-scale traffic data.
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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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Local Truncation Error-Guided Neural ODEs for Large Scale Traffic Forecasting
LTE-ODE repurposes local truncation error as an unsupervised dynamic attention mask that preserves continuous Neural ODE evolution in stable regions while triggering discrete compensation only at anomaly points in large-scale traffic data.