Summary of Automated Data Augmentation For Few-shot Time Series Forecasting: a Reinforcement Learning Approach Guided by a Model Zoo, By Haochen Yuan et al.
Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo
by Haochen Yuan, Yutong Wang, Yihong Chen, Yunbo Wang
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to time series data augmentation using reinforcement learning (RL). The method, called ReAugment, addresses the challenge of limited high-quality training data for few-shot learning scenarios. Specifically, it maintains a forecasting model zoo and identifies overfit-prone samples by measuring prediction diversity across models. These anchor points are then used as input for RL-based data augmentation, which enhances training set diversity and directs the augmented data to target regions where models are prone to overfitting. The paper demonstrates the effectiveness of ReAugment across various base models in standard time series forecasting and few-shot learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about improving time series forecasting, especially when there’s limited training data available. They created a new way to generate more training data using a technique called reinforcement learning (RL). The goal is to help forecasting models learn better by generating new data that’s relevant and useful for them. The method works by identifying areas where the models tend to make mistakes and then generates new data that targets those areas. This approach shows promise in improving forecasting accuracy, especially when there’s limited training data available. |
Keywords
» Artificial intelligence » Data augmentation » Few shot » Overfitting » Reinforcement learning » Time series
