Overview
- Features data-driven modeling algorithms for different industrial prediction requirements
- Discusses multi-scale (short, median, long) prediction, multi-type prediction (time series and factor-based), and interval-based prediction
- Includes case studies based on real-world industrial predictions
Part of the book series: Information Fusion and Data Science (IFDS)
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About this book
This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals withinthe machine learning and data analysis and mining communities.
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Keywords
Table of contents (9 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the authors
Jun Zhao is currently a Professor with the School of Control Science and Engineering, Dalian University of Technology, China.
Chunyang Sheng is currently a lecturer with the School of Electrical Engineering and Automation, Shandong University of Science and Technology, China.
Wei Wang is currently a Professor with the School of Control Science and Engineering, Dalian University of Technology, China.
Bibliographic Information
Book Title: Data-Driven Prediction for Industrial Processes and Their Applications
Authors: Jun Zhao, Wei Wang, Chunyang Sheng
Series Title: Information Fusion and Data Science
DOI: https://doi.org/10.1007/978-3-319-94051-9
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-94050-2Published: 30 August 2018
Softcover ISBN: 978-3-030-06785-4Published: 22 December 2018
eBook ISBN: 978-3-319-94051-9Published: 20 August 2018
Series ISSN: 2510-1528
Series E-ISSN: 2510-1536
Edition Number: 1
Number of Pages: XVI, 443
Number of Illustrations: 39 b/w illustrations, 128 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Manufacturing, Machines, Tools, Processes, Artificial Intelligence, Quality Control, Reliability, Safety and Risk, Operations Research/Decision Theory