{"slug": "llms-as-master-forgers-generating-synthetic-time-series-data-for-manufacturing", "title": "LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing", "summary": "A new arXiv paper (2609.16155v1) presents a framework that fine-tunes pre-trained Large Language Models and applies Retrieval Augmented Generation (RAG) to generate synthetic time series data for manufacturing processes, addressing the scarcity of labeled time-series data that limits robust machine learning models. The authors report that their LLM-driven method outperformed traditional baselines including ARIMA and LSTMs on quantitative metrics, PCA analysis, and downstream anomaly detection, generating synthetic data that captures the temporal dependencies and statistical properties of real manufacturing data.", "body_md": "arXiv:2609.16155v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/llms-as-master-forgers-generating-synthetic-time-series-data-for-manufacturing", "canonical_source": "https://arxiv.org/abs/2609.16155", "published_at": "2026-09-16 04:00:00+00:00", "updated_at": "2026-09-16 04:07:07.379610+00:00", "lang": "en", "topics": ["large-language-models", "machine-learning", "generative-ai", "ai-research"], "entities": ["arXiv", "Large Language Models", "Retrieval Augmented Generation", "ARIMA", "LSTMs"], "alternates": {"html": "https://wpnews.pro/news/llms-as-master-forgers-generating-synthetic-time-series-data-for-manufacturing", "markdown": "https://wpnews.pro/news/llms-as-master-forgers-generating-synthetic-time-series-data-for-manufacturing.md", "text": "https://wpnews.pro/news/llms-as-master-forgers-generating-synthetic-time-series-data-for-manufacturing.txt", "jsonld": "https://wpnews.pro/news/llms-as-master-forgers-generating-synthetic-time-series-data-for-manufacturing.jsonld"}}