用扩散Transformer生成可变长度的异构表格时间序列，效果显著优于现有方法。
Diffusion Transformers for Tabular Data Time Series Generation
- 基于扩散Transformer框架，统一处理异构表格数据与变长序列问题。
- 在6个数据集上实验表明，生成质量远超此前方法。
- 适合需要高保真表格时序数据生成的研究者或工业应用。
表格数据生成因多样应用场景而日益受关注，但生成依赖关系复杂的表格时间序列仍属未充分探索领域。这主要源于需同时解决表格数据异构性（常见于非时序方法）和序列长度可变性两大挑战。本文提出一种基于扩散Transformer（DiTs）的表格时间序列生成方法，借鉴其在图像与视频生成中的成功经验，扩展以应对异构数据与可变长度序列。在六个数据集上的大量实验表明，该方法显著优于已有工作。
原文摘要 · Abstract (English)
Tabular data generation has recently attracted a growing interest due to its different application scenarios. However, generating time series of tabular data, where each element of the series depends on the others, remains a largely unexplored domain. This gap is probably due to the difficulty of jointly solving different problems, the main of which are the heterogeneity of tabular data (a problem common to non-time-dependent approaches) and the variable length of a time series. In this paper, we propose a Diffusion Transformers (DiTs) based approach for tabular data series generation. Inspired by the recent success of DiTs in image and video generation, we extend this framework to deal with heterogeneous data and variable-length sequences. Using extensive experiments on six datasets, we show that the proposed approach outperforms previous work by a large margin.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。