A Preferences Corpus and Annotation Scheme for Human-Guided Alignment of Time-Series GPTs

Ricardo A. Calix (), Tyamo Okosun, Chenn Zhou and Hong Wang
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Ricardo A. Calix: Department of Computer Information Technology, Purdue University Northwest, Hammond, IN 46323, USA
Tyamo Okosun: Center for Innovation Through Visualization and Simulation (CIVS) and Steel Manufacturing Simulation and Visualization Consortium (SMSVC), Purdue University Northwest, Hammond, IN 46323, USA
Chenn Zhou: Center for Innovation Through Visualization and Simulation (CIVS) and Steel Manufacturing Simulation and Visualization Consortium (SMSVC), Purdue University Northwest, Hammond, IN 46323, USA
Hong Wang: Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA

Data, 2025, vol. 10, issue 10, 1-18

Abstract: The process of time-series forecasting such as predicting trajectories of silicon content in blast furnaces is a difficult task. Most time-series approaches today focus on scalar-type MSE loss optimization. This optimization approach, while widely common, could benefit from the use of human expert or process-level preferences. In this paper, we introduce a novel alignment and fine-tuning approach that involves learning from a corpus of preferred and dis-preferred time-series prediction trajectories. Our contributions include (1) a preference annotation pipeline for time-series forecasts, (2) the application of Score-based Preference Optimization (SPO) to train decoder-only transformers from preferences, and (3) results showing improvements in forecast quality. The approach is validated on both proprietary blast furnace data and the UCI Appliances Energy dataset. The proposed preference corpus and training strategy offer a new option for fine-tuning sequence models in industrial settings.

Keywords: time-series forecasting; preference learning; transformer models; GPT; DPO; industrial AI (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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