aiqbench analysis
ReAugment introduces a reinforcement learning framework that strategically augments time series datasets by targeting specific regions where base models are prone to overfitting. By leveraging a model zoo to identify prediction diversity gaps, this method effectively enhances few-shot forecasting performance across various architectures. This research advances the practical application of RL in improving data efficiency for sequential prediction tasks.
ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting
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. To address this, we present a pilot study on using reinforcement learning (RL) for time series data augmentation. Our method, ReAugment, tackles three critical questions: which parts of the training set should be augmented, how the augmentation should be performed, and what advantages RL brings to the process. Specifically, our approach maintains a forecasting model zoo, and by measuring prediction diversity across the models, we identify samples with higher probabilities for overfitting and use them as the anchor points for augmentation. Leveraging RL, our method adaptively transforms the overfit-prone samples into new data that not only enhances training set diversity but also directs the augmented data to target regions where the forecasting models are prone to overfitting. We validate the effectiveness of ReAugment across a wide range of base models, showing its advantages in both standard time series forecasting and few-shot learning tasks.
Abstract:Time series forecasting, particularly in few-shot learning scenarios, is challenging due to the limited availability of high-quality training data. To address this, we present a pilot study on using reinforcement learning (RL) for time series data augmentation. Our method, ReAugment, tackles three critical questions: which parts of the training set should be augmented, how the augmentation should be performed, and what advantages RL brings to the process. Specifically, our approach maintains a forecasting model zoo, and by measuring prediction diversity across the models, we identify samples with higher probabilities for overfitting and use them as the anchor points for augmentation. Leveraging RL, our method adaptively transforms the overfit-prone samples into new data that not only enhances training set diversity but also directs the augmented data to target regions where the forecasting models are prone to overfitting. We validate the effectiveness of ReAugment across a wide range of base models, showing its advantages in both standard time series forecasting and few-shot learning tasks.
Machine Learning (cs.LG)
arXiv:2409.06282 [cs.LG]
(or arXiv:2409.06282v5 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2409.06282 Focus to learn more arXiv-issued DOI via DataCite
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