LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting
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 alignment pipelines for each scenario is costly. Such settings often provide variable tables and process documents that record variable names, units, physical meanings, and process roles. However, standard time-series backbones usually treat inputs as anonymous numerical columns. Existing text-enhanced methods also rarely make the semantic-logical relations between input variables and the prediction target available to the model within each numerical window. To ad...
| Authors | Youcheng Zong, Runda Jia, Mingxuan Ren et al. |
|---|---|
| Primary category | cs.LG |
| Published | 7 July 2026 |
| Last updated at source | 18 July 2026 |
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