Proposes a matured-ground-truth TTA protocol and Frequency-Aware Calibration (FAC) that achieves competitive performance with substantially fewer parameters than prior TSF-TTA adapters.
Dish-ts: a general paradigm for alleviating distribution shift in time series forecasting, in: Proceedings of the 37th AAAI Conference on Artificial Intelligence
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This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.
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Towards Principled Test-Time Adaptation for Time Series Forecasting
Proposes a matured-ground-truth TTA protocol and Frequency-Aware Calibration (FAC) that achieves competitive performance with substantially fewer parameters than prior TSF-TTA adapters.
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Out-of-Distribution Generalization in Time Series: A Survey
This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.