LLM-derived measurement semantics enable a lightweight pre-inference correction step that reduces industrial prediction MAE by 30.7% on real tests and 80.3% under controlled sensor corruption.
TimeCMA: Towards LLM-empowered multivariate time series forecasting via cross-modality alignment,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
CONDITIONAL 2representative citing papers
TSF converts process variable documents into frozen semantic vectors that scale the numerical input window before a time-series backbone, yielding average MAE reductions of 2.9–3.6% across industrial forecasting tasks.
citing papers explorer
-
LLM-Guided Measurement Credibility Correction for Trustworthy Industrial Process Inference
LLM-derived measurement semantics enable a lightweight pre-inference correction step that reduces industrial prediction MAE by 30.7% on real tests and 80.3% under controlled sensor corruption.
-
LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting
TSF converts process variable documents into frozen semantic vectors that scale the numerical input window before a time-series backbone, yielding average MAE reductions of 2.9–3.6% across industrial forecasting tasks.