ReAugment：基于模型库的小样本时间序列增强与预测强化学习方法
ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting
AI总结：
ReAugment是基于模型库的强化学习方法，用于小样本时间序列增强与预测，可识别易过拟合样本并自适应生成新数据，提升预测效果。
摘要：
时间序列预测，尤其在小样本学习场景中，因高质量训练数据有限而极具挑战性。为解决该问题，我们开展了一项关于使用强化学习（RL）进行时间序列数据增强的初步研究。我们的方法ReAugment解决三个关键问题：训练集的哪些部分需要增强、应如何执行增强，以及RL在该过程中带来什么优势。具体而言，我们的方法维护一个预测模型库，通过测量各模型间的预测多样性，识别出更可能过拟合的样本，并将其用作增强的锚点。借助RL，我们的方法自适应地将易过拟合样本转换为新数据，这些新数据不仅能增强训练集多样性，还能将增强后的数据引导至预测模型易过拟合的目标区域。我们在多种基础模型上验证了ReAugment的有效性，展示了其在标准时间序列预测和小样本学习任务中的优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
