用强化学习控制扩散模型，为小数据时间序列生成更有效的增强数据。
DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data
- 扩散模型结合强化学习，动态生成提升预测精度的时序数据。
- 在六个真实数据集上，五组实验验证其显著提升预测效果。
- 适合数据稀缺场景，尤其适用于对生成质量要求高的时间序列任务。
小规模数据是时间序列预测中的关键挑战。数据增强虽有效，但难以生成有意义的数据。为此，我们提出 DAD4TS，一种基于扩散模型与强化学习的时间序列数据增强方法，专为小规模数据的预测任务设计。在 DAD4TS 中，数据生成器与时间序列模型联合训练，并由强化学习模型控制，以高效生成能提升预测准确率的样本。为适应小规模数据，我们采用数学方法而非传统变分自编码器（VAE）方法，通过将时序数据投影至几何空间来训练扩散模型。我们在六个真实世界数据集和八种时间序列模型上，通过定性与定量实验，对比七种基准方法，验证了 DAD4TS 的有效性。结果显示，DAD4TS 在五个数据集上表现优异。
原文摘要 · Abstract (English)
Small-scale data is a critical problem in time-series forecasting tasks. Data augmentation is an effective strategy for this task, but it has a limitation in generating meaningful data. To address this limitation, we propose DAD4TS, a diffusion-model-based data augmentation method with reinforcement learning, designed for time-series forecasting with small-scale data. In DAD4TS, a data generator is simultaneously trained with a time-series model and controlled by a reinforcement learning model to efficiently generate samples that improve the forecast accuracy of the time-series model. To support small-scale data, we use mathematical methods instead of conventional VAE methods to train the diffusion model by projecting the time-series data into the geometric space. We validated the effectiveness of DAD4TS with seven comparative methods through qualitative and quantitative experiments on six real-world datasets and eight time-series models. As a result, DAD4TS was validated on five datasets.
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