  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.
