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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

reinforcement-learning

arXiv AI

Aug 3

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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reinforcement-learning fine-tuning benchmarks

arXiv AI

Oct 9

ROAR: Retrieval Opportunity-Aware Refinement for Zero-Shot Time Series Forecasting

ROAR is a Retrieval Opportunity-Aware Refinement framework designed for zero-shot time series forecasting. It enhances predictions by weighting queries according to base-forecast difficulty and the improvement potential of retrieved historical candidates, then learning a gate to control correction strength. The method jointly calibrates the forecasting module and gate, anchoring the final prediction to an initial refined output, and achieves the lowest average MSE across seven benchmarks compared to other methods.

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rag benchmarks

arXiv AI

Jul 28

Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting

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.

By Xinghong Fu, Yanhong Li, Georgios Papaioannou, Yoon Kim

llms

arXiv Machine Learning

Jun 2

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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llms

arXiv Machine Learning

Sep 10

PatchFormer: A Patch-Based Time Series Foundation Model with Hierarchical Masked Reconstruction and Cross-Domain Transfer Learning for Zero-Shot Multi-Horizon Forecasting

arXiv:2601.20845v2 Announce Type: replace Abstract: Time series forecasting is a fundamental problem with applications in climate, energy, healthcare, and finance. Many existing approaches require do...

By Olaf Yunus Laitinen Imanov, Derya Umut Kulali, Taner Yilmaz

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

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Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification

arXiv:2602. 16224v2 Announce Type: replace Abstract: Time series data are prone to noise in various domains, and training samples may contain low-predictability patterns that deviate from the normal data distribution, leading to training instability or convergence to poor local minima.

By Xu Zhang, Peng Wang, Yichen Li, Wei Wang

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

ROAR: Retrieval Opportunity-Aware Refinement for Zero-Shot Time Series Forecasting

Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting

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Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification