An LSTM-based predictive monitoring method for data with time-varying variability

Jiaqi Qiu, Yu Lin and Inez M. Zwetsloot

International Journal of Production Research, 2025, vol. 63, issue 7, 2622-2637

Abstract: The recurrent neural network and its variants have shown great success in processing sequences in recent years. However, these neural networks have not aroused much attention in the process monitoring literature. Whereas the flexibility and outstanding performance of these models enable them to be solutions to many problem settings within process monitoring. One such problem setting is process data with inherent time-varying variability, an aspect of data that is often ignored. Therefore, this paper proposes a prospective monitoring method for detecting structural changes in data with inherent time-varying variability. We integrate long short-term memory (LSTM) prediction intervals with a neural network (NN) for variability prediction to detect structural changes in data online. We compare our proposed method to benchmarks in a simulation study, showing that the proposed model outperforms other NN-based predictive monitoring methods for mean shift detection. The proposed method is also applied in two case studies to sensor data, which confirms that the proposed method is an effective technique for detecting shifts.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2024.2408435 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:63:y:2025:i:7:p:2622-2637

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2024.2408435

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().