With the remarkable success of generative models like ChatGPT, Artificial Intelligence Generated Content (AIGC) is undergoing explosive development. Not limited to text and images, generative models can generate industrial time series data, addressing challenges such as the difficulty of data collection and data annotation. Due to their outstanding generation ability, they have been widely used in Internet of Things, metaverse, and cyber-physical-social systems to enhance the efficiency of industrial production. In this paper, we present a comprehensive overview of generative models for industrial time series from deep generative models (DGMs) to large generative models (LGMs). First, a DGM-based AIGC framework is proposed for industrial time series generation. Within this framework, we survey advanced industrial DGMs and present a multi-perspective categorization. Furthermore, we systematically analyze the critical technologies required to construct industrial LGMs from four aspects: large-scale industrial dataset, LGMs architecture for complex industrial characteristics, self-supervised training for industrial time series, and fine-tuning of industrial downstream tasks. Finally, we conclude the challenges and future directions to enable the development of generative models in industry.

翻译：随着ChatGPT等生成模型的显著成功，人工智能生成内容（AIGC）正在经历爆炸式发展。生成模型不限于文本和图像，也能生成工业时间序列数据，以应对数据收集困难和数据标注等挑战。凭借其出色的生成能力，它们已被广泛应用于物联网、元宇宙和信息物理社会系统，以提高工业生产效率。本文全面综述了面向工业时间序列的生成模型，涵盖从深度生成模型（DGMs）到大型生成模型（LGMs）的发展。首先，我们提出了一个基于DGM的AIGC框架用于工业时间序列生成。在此框架内，我们梳理了先进的工业DGMs，并提出了一个多视角的分类体系。此外，我们从四个方面系统分析了构建工业LGMs所需的关键技术：大规模工业数据集、面向复杂工业特性的LGMs架构、工业时间序列的自监督训练以及工业下游任务的微调。最后，我们总结了推动生成模型在工业领域发展的挑战与未来方向。