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LLMs Enhance Industrial Forecasting by Integrating Semantic Process Knowledge.

Youcheng Zong, Runda Jia, Mingxuan Ren, Dakuo He· July 9, 2026 View original

Summary

A new framework, TSF, uses Large Language Models (LLMs) to build a "task-semantic field" from process documents, integrating semantic information into time-series forecasting for industrial processes. This improves accuracy and adaptability without increasing online inference overhead.

Industrial processes often struggle with time-series forecasting and soft sensing due to scarce labeled data, frequent regime changes, and the high cost of retraining models. While process documents contain valuable semantic information about variables and their roles, standard time-series models typically treat inputs as anonymous numerical data, failing to leverage this context. This paper introduces Task-Semantic Field Factorization (TSF), a framework designed to bridge this gap. TSF employs an LLM offline to construct a comprehensive "task-semantic field" from existing task protocols and variable documentation. This semantic field is then integrated into conventional time-series backbones during training and inference. By activating variable semantics within each numerical window, TSF ensures that semantic information actively participates in every prediction, enhancing adaptability to different prediction targets and operating shifts. Experiments across various industrial forecasting tasks show TSF significantly reduces Mean Absolute Error (MAE) with minimal additional parameters and negligible online inference overhead, effectively turning existing process documents into measurable performance gains.

Why it matters

This approach allows industrial companies to leverage their existing documentation to significantly improve the accuracy and adaptability of their forecasting and soft-sensing models, leading to better operational decisions.

How to implement this in your domain

  1. 1Audit existing industrial process documentation to identify valuable semantic information for model enhancement.
  2. 2Experiment with LLM-guided semantic extraction and integration into current time-series forecasting pipelines.
  3. 3Develop a strategy for maintaining and updating the "task-semantic field" as process documentation evolves.
  4. 4Pilot TSF or similar semantic integration methods on a specific industrial forecasting challenge to quantify benefits.

Who benefits

Process ManufacturingChemical EngineeringOil & GasPharmaceuticalsSmart Factories

Key takeaways

  • TSF uses LLMs to extract and integrate semantic knowledge from process documents into time-series models.
  • This semantic integration improves forecasting accuracy and adaptability in industrial settings.
  • The LLM is used offline for semantic construction, keeping online inference lightweight.
  • TSF offers a cost-effective way to leverage existing documentation for measurable performance gains.

Original post by Youcheng Zong, Runda Jia, Mingxuan Ren, Dakuo He

"arXiv:2607.06623v1 Announce Type: new Abstract: Process industries rely on time-series forecasting and soft sensing to estimate quality variables that are hard to measure online. Labeled data are scarce, operating regimes change frequently, and retraining models or rebuilding ali…"

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Originally posted by Youcheng Zong, Runda Jia, Mingxuan Ren, Dakuo He on X · view source

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