AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Models

• URL: http://arxiv.org/abs/2407.11480v1
• Date: Tue, 16 Jul 2024 08:16:54 GMT
• Title: AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Models
• Authors: Lei Ren, Haiteng Wang, Yang Tang, Chunhua Yang,
• Abstract summary: 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 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.
• Score: 10.861135490840725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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