arXiv Machine Learning By Mohamed Readh Fentazi, Mazene Ameur, Adlen Ksentini

Training on the Future: A Delay-Aware Audit of Test-Time Adaptation for Time-Series Forecasting

Read the original on arXiv Machine Learning →

arXiv:2610. 12232v1 Announce Type: new Abstract: Test-time adaptation (TTA) methods for time-series forecasting update a deployed model, or a small adapter around it, from incoming ground truth.

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arXiv Machine Learning
Jul 2

LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning

arXiv:2607. 00958v1 Announce Type: new Abstract: Time series are central to modern data mining applications, from industrial telemetry and server metrics to finance and physiology, yet time-series self-supervised learning often depends on view and augmentation choices that encode domain-specific invariances.

By Alexander Chemeris, Ming Jin, Randall Balestriero