Polygon generation and video-to-video translation for time-series prediction

Mohamed Elhefnawy (), Ahmed Ragab () and Mohamed-Salah Ouali ()
Additional contact information
Mohamed Elhefnawy: Polytechnique Montréal
Ahmed Ragab: Polytechnique Montréal
Mohamed-Salah Ouali: Polytechnique Montréal

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 1, No 12, 279 pages

Abstract: Abstract This paper proposes an innovative method for time-series prediction in energy-intensive industrial systems characterized by highly dynamic non-linear operations. The proposed method can capture the true distributions of the inputs and outputs of such systems and map these distributions using polygon generation and video-to-video translation techniques. More specifically, the time-series data are represented as polygon streams (videos), then the video-to-video translation is used to transform the input polygon streams into the output ones. This transformation is tuned based on a model trustworthiness metric for optimal video synthesis. Finally, an image processing procedure is used for mapping the output polygon streams back to time-series outputs. The proposed method is based on cycle-consistent generative adversarial networks as an unsupervised approach. This does not need the heavy involvement of the human expert who devotes much effort to labeling the complex industrial data. The performance of the proposed method was validated successfully using a challenging industrial dataset collected from a complex heat exchanger network in a Canadian pulp mill. The results obtained using the proposed method demonstrate better performance than other comparable time-series prediction models. This allows process operators to accurately monitor process key performance indicators (KPIs) and to achieve a more energy-efficient operation.

Keywords: Time-series prediction; Polygon generation; Video-to-video translation; Generative adversarial network (GAN); Data streams; Deep learning (DL) (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-022-02003-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:joinma:v:34:y:2023:i:1:d:10.1007_s10845-022-02003-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-022-02003-1

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().