ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting

TL;DR

ReAugment employs reinforcement learning to intelligently augment limited time series data by identifying overfit-prone samples, thereby improving forecasting accuracy in few-shot learning scenarios.

Contribution

This paper introduces ReAugment, a novel RL-based method that guides data augmentation in time series forecasting by leveraging a model zoo and prediction diversity.

Findings

ReAugment improves forecasting accuracy across various models.

The method enhances data diversity and reduces overfitting.

Effective in both standard and few-shot time series 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.