动态生成合成数据，让时间序列大模型训练更高效
OATS: Online Data Augmentation for Time Series Foundation Models
- 根据训练阶段动态生成适配的合成数据
- 在6个数据集上显著超越静态增强方法
- 适合做时间序列建模与数据增强的研究者
时间序列基础模型（TSFMs）是时间序列分析的强大范式，常通过合成数据增强来提升训练质量。现有方法多依赖启发式规则和静态策略。受动态数据优化启发——样本贡献随训练阶段变化——我们提出OATS（面向时间序列基础模型的在线数据增强），一种针对不同训练阶段生成定制化合成数据的系统性策略。OATS利用有价值训练样本作为指导信号，动态生成高质量合成数据。我们设计基于扩散的框架生成真实时间序列，并引入探索-利用机制平衡效率与效果。在多个时间序列基础模型上的实验表明，OATS始终优于常规训练，在六个验证数据集和两种模型架构上均显著超越静态数据增强基线。代码已开源：https://github.com/microsoft/TimeCraft。
原文摘要 · Abstract (English)
Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to improve the training data quality. Existing augmentation methods, however, typically rely on heuristics and static paradigms. Motivated by dynamic data optimization, which shows that the contribution of samples varies across training stages, we propose OATS (Online Data Augmentation for Time Series Foundation Models), a principled strategy that generates synthetic data tailored to different training steps. OATS leverages valuable training samples as principled guiding signals and dynamically generates high-quality synthetic data conditioned on them. We further design a diffusion-based framework to produce realistic time series and introduce an explore-exploit mechanism to balance efficiency and effectiveness. Experiments on TSFMs demonstrate that OATS consistently outperforms regular training and yields substantial performance gains over static data augmentation baselines across six validation datasets and two TSFM architectures. The code is available at the link https://github.com/microsoft/TimeCraft.
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