Evaluating Large Language Models for Turboshaft Engine Torque Prediction
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Abstract
Recent advancements in deep learning have introduced new opportunities for quality management in manufacturing, particularly through transformer-based architectures capable of learning from limited datasets and handling complex, multimodal inputs. Among these, Large Language Models (LLMs) have emerged as a significant innovation, demonstrating strong capabilities in forecasting and representing the cutting edge of artificial intelligence (AI). Through transfer learning, LLMs effectively process and generate extended text sequences, and recent developments show their potential for multimodal integration, including text, images, audio, and video data.
Quality management is a critical area for industrial innovation, rapidly evolving as manufacturers seek to close the quality-manufacturing loop and achieve zero-defect production goals. While computer vision techniques based on deep learning have been widely implemented for visual inspection tasks, integrating multiple heterogeneous data sources offers the possibility for even greater improvements. Despite the success of LLMs in language tasks, their application to time series data remains relatively unexplored. Alternative statistical approaches and deep learning models have proven effective for time series forecasting. Nevertheless, LLMs could provide additional advantages in industrial contexts, offering opportunities to enhance in-line quality control, defect prevention, and predictive discarding strategies across various sectors.
This paper investigates the potential of applying LLMs to time series analysis by comparing the performance of an LLM (GPT-2), originally trained on textual data, with a model specifically designed for time series data (TimeGPT), and a more conventional transformer-based architecture. Our study includes a dedicated time series GPT model and a general-purpose LLM in a comparative evaluation. Through this analysis, we aim to better understand how language models can be effectively adapted to time series forecasting tasks and explore their transfer learning potential for enhancing quality management in manufacturing.
How to Cite
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LLMs, Time series forecasting, Quality management
Cao, D., Jia, F., Arik, S. O., Pfister, T., Zheng, Y., Ye, W., & Liu, Y. (2023). Tempo: Prompt-based generative pre-trained transformer for time series forecasting.†arXiv preprint arXiv:2310.04948.
Cao, R., & Wang, Q. (2024). An evaluation of standard statistical models and llms on time series forecasting.†arXiv preprint arXiv:2408.04867.
Chang, C., Wang, W. Y., Peng, W. C., & Chen, T. F. (2025). Llm4ts: Aligning pre-trained llms as data-efficient time-series forecasters.†ACM Transactions on Intelligent Systems and Technology,†16(3), 1-20.
Deng, H., Luo, H., Zhai, W., Cao, Y., & Kang, Y. (2024). Vmad: Visual-enhanced multimodal large language model for zero-shot anomaly detection.†arXiv preprint arXiv:2409.20146.
Garza, A., Challu, C., & Mergenthaler-Canseco, M. (2023). TimeGPT-1.†arXiv preprint arXiv:2310.03589.
Jiang, X., Li, J., Deng, H., Liu, Y., Gao, B. B., Zhou, Y., ... & Zheng, F. (2024). Mmad: The first-ever comprehensive benchmark for multimodal large language models in industrial anomaly detection.†arXiv preprint arXiv:2410.09453.
He, D., Bechhoefer, E., & Hess, A. (2025). Automated rotorcraft turboshaft engine performance prediction using a transfer learning approach. IEEE Aerospace Conference, Mar. 1ñ8, Big Sky, MT, USA.
He, D., He, M., & Taffari, A. (2023). Few-shot learning for full ceramic bearing fault diagnosis with acoustic emission signals. PHM Society Asia-Pacific Conference†(Vol. 4, No. 1), Sept. 11ñ14, Tokyo, Japan.
Kafritsas, N. (2024, Dec. 17). Temporal fusion transformer: Time series forecasting with interpretability. AI Horizon Forecast. https://aihorizonforecast.substack.com/p/ temporal-fusion-transformer-time
Koti, V. (2024, Sept. 12). From theory to code: step-by-step implementation and code breakdown of GPT-2 model. Medium. https://medium.com/@vipul.koti333/from-theory-to-code-step-by-step-implementation-and-code-breakdown-of-gpt-2-model-7bde8d5cecda
Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: A survey.†Philosophical Transactions of the Royal Society A,†379(2194), 20200209.
Paniccia, D., Tucci, F. A., Guerrero, J., Capone, L., Sanguini, N., Benacchio, T., & Bottasso, L. (2025). A supervised machine-learning approach for turboshaft engine dynamic modeling under real flight conditions.†arXiv preprint arXiv:2502.14120.
Simhayev, E., Rasul, K., & Rogge, N. (2023, Jun. 16). Yes, transformers are effective for time series forecasting (+ autoformer). Hugging Face. https://huggingface.co/ blog/autoformer
Simhayev, E., Rogge, N., & Rasul, K. (2023, Mar. 10). Multivariate probabilistic time series forecasting with informer. Hugging Face. https://huggingface.co/blog/ informer
Soydaner, D. (2022). Attention mechanism in neural networks: where it comes and where it goes.†Neural Computing and Applications,†34(16), 13371-13385.
Tang, H., Zhang, C., Jin, M., Yu, Q., Wang, Z., Jin, X., ... & Du, M. (2025). Time series forecasting with llms: Understanding and enhancing model capabilities.†ACM SIGKDD Explorations Newsletter,†26(2), 109-118.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Wikipedia (2024, Mar. 22). Full GPT architecture. https://commons.wikimedia.org/wiki/File:Full_GPT_architecture.svg
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021). Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence†(Vol. 35, No. 12, pp. 11106-11115).
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