用强化学习自动选关键数据增强，提升小样本时间序列预测效果
ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting
- 构建模型库，通过预测多样性识别易过拟合样本
- 强化学习动态生成新数据，显著提升训练集多样性
- 适用于小样本场景，对多种预测模型都有效
时间序列预测在小样本学习场景下因高质量训练数据稀缺而面临挑战。为此，我们首次探索利用强化学习（RL）进行时间序列数据增强。提出的ReAugment方法解决三个核心问题：应增强训练集的哪些部分、如何实施增强、以及强化学习带来的优势。具体而言，该方法维护一个预测模型集合，通过测量各模型间的预测多样性，识别出高过拟合风险的样本，并将其作为增强的锚点。借助强化学习，该方法自适应地将这些易过拟合样本转化为新数据，不仅增强了训练集多样性，还引导新增数据聚焦于模型易过拟合的区域。我们在多种基础模型上验证了ReAugment的有效性，结果表明其在标准时间序列预测与小样本学习任务中均表现优异。
原文摘要 · Abstract (English)
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.
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