arXiv Machine Learning August 21, 2026 By Haochen Yuan, Yutong Wang, Yihong Chen, Yunbo Wang, Xiaokang Yang

ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting

Read the original on arXiv Machine Learning →

arXiv:2409. 06282v5 Announce Type: replace Abstract: Time series forecasting, particularly in few-shot learning scenarios, is challenging due to the limited availability of high-quality training data.

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reinforcement-learning

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TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning

arXiv:2605. 00015v2 Announce Type: replace-cross Abstract: Time Series Foundation Models (TSFMs) have demonstrated strong generalization capability and data efficiency in time series forecasting through large-scale pretraining.

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arXiv:2602. 17634v2 Announce Type: replace-cross Abstract: Learning time series foundation models has been shown to be a promising approach for zero-shot time series forecasting across diverse time series domains.

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Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting

arXiv:2606. 01289v1 Announce Type: new Abstract: Zero-shot time series forecasting aims to predict future values for previously unseen series, requiring models to generalize temporal dynamics beyond the training distribution.

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DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data

arXiv:2605. 17866v2 Announce Type: replace Abstract: Small-scale data is a critical problem in time-series forecasting tasks.

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