In this paper, we leverage the power of latent diffusion models to generate
synthetic time series tabular data. Along with the temporal and feature
correlations, the heterogeneous nature of the feature in the table has been one
of the main obstacles in time series tabular data modeling. We tackle this
problem by combining the ideas of the variational auto-encoder (VAE) and the
denoising diffusion probabilistic model (DDPM). Our model named as
\texttt{TimeAutoDiff} has several key advantages including (1) Generality: the
ability to handle the broad spectrum of time series tabular data from single to
multi-sequence datasets; (2) Good fidelity and utility guarantees: numerical
experiments on six publicly available datasets demonstrating significant
improvements over state-of-the-art models in generating time series tabular
data, across four metrics measuring fidelity and utility; (3) Fast sampling
speed: entire time series data generation as opposed to the sequential data
sampling schemes implemented in the existing diffusion-based models, eventually
leading to significant improvements in sampling speed, (4) Entity conditional
generation: the first implementation of conditional generation of
multi-sequence time series tabular data with heterogenous features in the
literature, enabling scenario exploration across multiple scientific and
engineering domains. Codes are in preparation for release to the public, but
available upon request.

通过结合变分自动编码器（VAE）和去噪扩散概率模型（DDPM）的思想，我们提出了一种名为 TimeAutoDiff 的模型，用于生成时间序列表格数据。该模型能够处理各种类型的时间序列表格数据，具有良好的保真度和实用性，并且在生成速度上有显著改进；此外，它还实现了对具有异构特征的多序列时间序列表格数据的有条件生成。