  Industrial Multivariate Time Series (MTS) is a critical view of the
industrial field for people to understand the state of machines. However, due
to data collection difficulty and privacy concerns, available data for building
industrial intelligence and industrial large models is far from sufficient.
Therefore, industrial time series data generation is of great importance.
Existing research usually applies Generative Adversarial Networks (GANs) to
generate MTS. However, GANs suffer from unstable training process due to the
joint training of the generator and discriminator. This paper proposes a
temporal-augmented conditional adaptive diffusion model, termed Diff-MTS, for
MTS generation. It aims to better handle the complex temporal dependencies and
dynamics of MTS data. Specifically, a conditional Adaptive Maximum-Mean
Discrepancy (Ada-MMD) method has been proposed for the controlled generation of
MTS, which does not require a classifier to control the generation. It improves
the condition consistency of the diffusion model. Moreover, a Temporal
Decomposition Reconstruction UNet (TDR-UNet) is established to capture complex
temporal patterns and further improve the quality of the synthetic time series.
Comprehensive experiments on the C-MAPSS and FEMTO datasets demonstrate that
the proposed Diff-MTS performs substantially better in terms of diversity,
fidelity, and utility compared with GAN-based methods. These results show that
Diff-MTS facilitates the generation of industrial data, contributing to
intelligent maintenance and the construction of industrial large models.
