ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting
This addresses data scarcity in time series forecasting, particularly for few-shot learning, though it appears incremental as it builds on existing RL and model zoo concepts.
The paper tackles the challenge of few-shot time series forecasting by proposing ReAugment, a reinforcement learning method for data augmentation that identifies overfit-prone samples and transforms them to enhance training diversity, resulting in improved performance across various base models in both standard and few-shot scenarios.
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.