Authors:
- 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)
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (9 chapters)
-
Front Matter
-
Back Matter
About this book
Keywords
Authors and Affiliations
-
Dalian University of Technology, Dalian, China
Jun Zhao, Wei Wang
-
Shandong University of Science and Technology, Qingdao, China
Chunyang Sheng
About the authors
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