TimeGPT-based multi-step-ahead key quality indicator forecasting for industrial processes

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice Pub Date : 2025-06-04 DOI:10.1016/j.conengprac.2025.106410

Xinmin Zhang , Yuwei Chen , Bocun He , Zhihuan Song , Manabu Kano

{"title":"TimeGPT-based multi-step-ahead key quality indicator forecasting for industrial processes","authors":"Xinmin Zhang , Yuwei Chen , Bocun He , Zhihuan Song , Manabu Kano","doi":"10.1016/j.conengprac.2025.106410","DOIUrl":null,"url":null,"abstract":"<div><div>Multi-step prediction is one of the most challenging problems in the field of industrial soft sensing. Recently, large language models have been widely used in various fields. Inspired by TimeGPT, a popular large-scale model for time series forecasting, this paper proposes a novel multi-step key quality indicator forecasting method for industrial processes, namely the TimeGPT-based Multi-step-ahead Forecasting (TiMF) model. The proposed TiMF model is designed based on pre-trained TimeGPT, and historical process variable information is integrated into the prediction model as an auxiliary guide to improve the utilization of industrial data information. To evaluate the effectiveness of the proposed method, it was applied to the debutanizer industrial process and the sintering industrial process. The application results show that the proposed TiMF can achieve better prediction accuracy than other existing methods. This work provides a new attempt for the industrial soft sensing application of the large-scale time series prediction model.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106410"},"PeriodicalIF":4.6000,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S096706612500173X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

Multi-step prediction is one of the most challenging problems in the field of industrial soft sensing. Recently, large language models have been widely used in various fields. Inspired by TimeGPT, a popular large-scale model for time series forecasting, this paper proposes a novel multi-step key quality indicator forecasting method for industrial processes, namely the TimeGPT-based Multi-step-ahead Forecasting (TiMF) model. The proposed TiMF model is designed based on pre-trained TimeGPT, and historical process variable information is integrated into the prediction model as an auxiliary guide to improve the utilization of industrial data information. To evaluate the effectiveness of the proposed method, it was applied to the debutanizer industrial process and the sintering industrial process. The application results show that the proposed TiMF can achieve better prediction accuracy than other existing methods. This work provides a new attempt for the industrial soft sensing application of the large-scale time series prediction model.

查看原文本刊更多论文

基于时间gpt的工业过程多步关键质量指标预测

多步预测是工业软测量领域最具挑战性的问题之一。近年来，大型语言模型被广泛应用于各个领域。受时间序列预测常用的大尺度模型TimeGPT的启发，本文提出了一种新的工业过程关键质量指标多步预测方法，即基于TimeGPT的多步超前预测（multistep -ahead forecasting, TiMF）模型。提出的TiMF模型基于预训练的TimeGPT进行设计，并将历史过程变量信息集成到预测模型中作为辅助指导，提高工业数据信息的利用率。为验证该方法的有效性，将其应用于脱坦剂工业过程和烧结工业过程。应用结果表明，与现有方法相比，该方法具有更好的预测精度。本工作为大尺度时间序列预测模型的工业软测量应用提供了新的尝试。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Control Engineering Practice 工程技术-工程：电子与电气

CiteScore

9.20

自引率

12.20%

发文量

183

审稿时长

44 days

期刊介绍： Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper. The scope of Control Engineering Practice matches the activities of IFAC. Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.