AI 中文总结
该综述聚焦金融领域的扩散族生成模型，按金融数据类型分类梳理相关研究，是首个针对金融数据扩散族模型的专门综述，还开源了相关代码仓库。
AI 中文摘要
扩散生成模型已迅速成为建模复杂金融数据的强大工具，其吸引力兼具结构性与实用性：它们提供基于似然的稳定训练、强模式覆盖、灵活的条件设置，以及与金融领域广泛使用的伊藤微积分和随机控制框架自然契合的随机微分方程表述。本综述回顾了关于扩散族生成模型在金融应用方面日益增多的文献，我们主要按金融数据类型对现有研究进行组织，涵盖时间序列、限价订单簿、表格数据及其他结构化金融对象，同时讨论了每类数据中出现的建模目标与应用场景。据我们所知，这是首个专门针对金融数据的扩散族模型的综述。如需更详细信息，我们已开源了一个代码仓库：this https URL。
英文摘要
Diffusion generative models have rapidly emerged as powerful tools for modeling complex financial data. Their appeal is both structural and practical: they offer stable likelihood-based training, strong mode coverage, flexible conditioning, and a stochastic-differential-equation formulation that aligns naturally with the Itô calculus and stochastic control frameworks widely used in finance. This survey reviews the growing literature on diffusion-family generative models for financial applications. We organize prior work primarily by financial data type, covering time series, limit order books, tabular data, and other structured financial objects, while discussing the modeling goals and application contexts that arise within each category. To the best of our knowledge, this is the first survey dedicated specifically to diffusion-family models for financial data. For more detailed information, we have open-sourced a repository https://github.com/ZhuoHan1998/Diffusion-Models-In-Finance.
