Data-Driven Prediction for Industrial Processes and Their Applications
Authors: Zhao, Jun, Wang, Wei, Sheng, Chunyang
- 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
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- About this book
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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 within the machine learning and data analysis and mining communities.
- About the authors
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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.
- Table of contents (9 chapters)
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Introduction
Pages 1-11
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Data Preprocessing Techniques
Pages 13-52
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Industrial Time Series Prediction
Pages 53-119
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Factor-Based Industrial Process Prediction
Pages 121-157
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Industrial Prediction Intervals with Data Uncertainty
Pages 159-222
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Table of contents (9 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Data-Driven Prediction for Industrial Processes and Their Applications
- Authors
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- Jun Zhao
- Wei Wang
- Chunyang Sheng
- Series Title
- Information Fusion and Data Science
- Copyright
- 2018
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing AG, part of Springer Nature
- eBook ISBN
- 978-3-319-94051-9
- DOI
- 10.1007/978-3-319-94051-9
- Hardcover ISBN
- 978-3-319-94050-2
- Softcover ISBN
- 978-3-030-06785-4
- Series ISSN
- 2510-1528
- Edition Number
- 1
- Number of Pages
- XVI, 443
- Number of Illustrations
- 39 b/w illustrations, 128 illustrations in colour
- Topics
