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This research demonstrates how Large Language Models can effectively generate high-fidelity synthetic time-series data to address the scarcity of labeled datasets in manufacturing environments. By leveraging RAG techniques and fine-tuning on process instructions, the proposed framework outperforms traditional baselines like ARIMA and LSTMs in capturing complex temporal dependencies for anomaly detection tasks. The study highlights a significant advancement in using generative AI to augment industrial machine learning pipelines where real-world data is limited or sensitive.
LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing
arXiv:2609.16155v1 Announce Type: cross Abstract: This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. Motivated by the scarcity of labeled time-series data in real-world manufacturing settings, which hinders the development of robust machine learning models, we explore the potential of LLMs to learn complex temporal dependencies and generate realistic synthetic data. Our approach involves fine-tuning pre-trained LLMs on manufacturing process instructions and employing a Retrieval Augmented Generation (RAG) technique to enhance data diversity and realism. We evaluate our method against traditional time series modeling techniques like ARIMA and LSTMs, using quantitative metrics, PCA analysis, and downstream task performance (anomaly detection). Results demonstrate that our LLM-driven framework outperforms these baselines, generating high-quality synthetic time series data that effectively captures temporal dependencies and statistical properties of real manufacturing data, leading to improvements in downstream task performance.
Abstract:This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. Motivated by the scarcity of labeled time-series data in real-world manufacturing settings, which hinders the development of robust machine learning models, we explore the potential of LLMs to learn complex temporal dependencies and generate realistic synthetic data. Our approach involves fine-tuning pre-trained LLMs on manufacturing process instructions and employing a Retrieval Augmented Generation (RAG) technique to enhance data diversity and realism. We evaluate our method against traditional time series modeling techniques like ARIMA and LSTMs, using quantitative metrics, PCA analysis, and downstream task performance (anomaly detection). Results demonstrate that our LLM-driven framework outperforms these baselines, generating high-quality synthetic time series data that effectively captures temporal dependencies and statistical properties of real manufacturing data, leading to improvements in downstream task performance.
7 pages, 4 figures. Published in the 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2609.16155 [cs.LG]
(or arXiv:2609.16155v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.16155 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), pp. 2053-2059, IEEE, 2024
https://doi.org/10.1109/ICICML63543.2024.10958017 Focus to learn more DOI(s) linking to related resources
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