用分数生成模型合成时间序列，支持规则与不规则数据。
TSGM: Regular and Irregular Time-series Generation using Score-based Generative Models
- 基于条件分数匹配构建时序生成网络。
- 在多个数据集上达到顶尖生成质量与多样性。
- 适用于规则和不规则时序，模型改动极小。
分数生成模型（SGMs）在图像生成、语音合成、表格数据合成等领域展现出卓越的采样质量与多样性。受此启发，本文将SGMs应用于时序数据生成，通过学习其条件分数函数实现合成。为此，提出一种面向时序合成的条件分数网络，并设计了专用于该任务的去噪分数匹配损失。特别地，该损失为时序合成量身定制的条件去噪分数匹配损失。此外，框架具有高度灵活性，仅需微小调整即可生成规则与不规则时序数据。最终，在多个时序数据集上均取得优异表现，生成质量与多样性均达当前最优水平。
原文摘要 · Abstract (English)
Score-based generative models (SGMs) have demonstrated unparalleled sampling quality and diversity in numerous fields, such as image generation, voice synthesis, and tabular data synthesis, etc. Inspired by those outstanding results, we apply SGMs to synthesize time-series by learning its conditional score function. To this end, we present a conditional score network for time-series synthesis, deriving a denoising score matching loss tailored for our purposes. In particular, our presented denoising score matching loss is the conditional denoising score matching loss for time-series synthesis. In addition, our framework is such flexible that both regular and irregular time-series can be synthesized with minimal changes to our model design. Finally, we obtain exceptional synthesis performance on various time-series datasets, achieving state-of-the-art sampling diversity and quality.
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