
Predictive Data Mining Models
Springer (Publisher)
Published on 5. October 2016
Book
Hardback
XI, 102 pages
978-981-10-2542-6 (ISBN)
Article exhausted; check for reprint
Description
This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The book's main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access.
More details
Series
Edition
1st ed. 2017
Language
English
Place of publication
Singapore
Singapore
Target group
Professional and scholarly
Illustrations
105
6 s/w Abbildungen, 48 farbige Abbildungen, 105 farbige Tabellen
105 Tables, color; 48 Illustrations, color; 6 Illustrations, black and white; XI, 102 p. 54 illus., 48 illus. in color.
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Weight
371 gr
ISBN-13
978-981-10-2542-6 (9789811025426)
DOI
10.1007/978-981-10-2543-3
Schweitzer Classification
Other editions
New editions

David L. Olson | Desheng Wu
Predictive Data Mining Models
Book
08/2019
2nd Edition
Springer
€117.69
Shipment within 15-20 days
Additional editions

David L. Olson | Desheng Wu
Predictive Data Mining Models
Book
06/2018
Springer
€96.29
Shipment within 15-20 days

David L. Olson | Desheng Wu
Predictive Data Mining Models
E-Book
09/2016
Springer
€96.29
Available for download
Persons
David L. Olson is the James & H.K. Stuart Chancellor's Distinguished Chair and Full Professor at the University of Nebraska. He has published research in over 150 refereed journal articles, primarily on the topic of multiple-objective decision-making, information technology, supply chain risk management, and data mining. He teaches in the management information systems, management science, and operations management areas. He has authored over 20 books and is a member of the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society. He was a Lowry Mays endowed Professor at Texas A&M University from 1999 to 2001. He was named the Raymond E. Miles Distinguished Scholar for 2002, and was a James C. and Rhonda Seacrest Fellow from 2005 to 2006. He was named Best Enterprise Information Systems Educator by the IFIP in 2006 and is a Fellow of the Decision Sciences Institute.
Desheng Dash Wu is a distinguished professor at the University of Chinese Academy of Sciences. His research interests include enterprise risk management, performance evaluation, and decision support systems. His has published more than 80 journal papers in such journals as Production and Operations Management, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Risk Analysis, Decision Sciences, Decision Support Systems, European Journal of Operational Research, IEEE Transactions on Knowledge and Data Engineering, et al. He has coauthored 3 books with David L Olson, and has served as editor/guest editor for several journals such as IEEE Transactions on Systems, Man, and Cybernetics: Part B, Omega, Computers and OR, International Journal of Production Research.
Desheng Dash Wu is a distinguished professor at the University of Chinese Academy of Sciences. His research interests include enterprise risk management, performance evaluation, and decision support systems. His has published more than 80 journal papers in such journals as Production and Operations Management, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Risk Analysis, Decision Sciences, Decision Support Systems, European Journal of Operational Research, IEEE Transactions on Knowledge and Data Engineering, et al. He has coauthored 3 books with David L Olson, and has served as editor/guest editor for several journals such as IEEE Transactions on Systems, Man, and Cybernetics: Part B, Omega, Computers and OR, International Journal of Production Research.
Content
Chapter 1 Knowledge Management.- Chapter 2 Data Sets.- Chapter 3 Basic Forecasting ToolsChapter 3 Basic Forecasting Tools.- Chapter 4 Multiple Regression.- Chapter 5 Regression Tree Models.- Chapter 6 Autoregressive Models.- Chapter 7 GARCH Models.- Chapter 8 Comparison of Models.