Predictive Data Mining Models (eBook)

David L. Olson, Desheng Wu (Autoren)

eBook Download: PDF

2016 | 1st ed. 2017
XI, 102 Seiten
Springer Singapore (Verlag)
978-981-10-2543-3 (ISBN)

Infos

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.

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.

Inhalt

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.

Produkt-Details

• Erscheint lt. Verlag: 26.9.2016
• Reihe/Serie: Computational Risk Management
• Reihe/Serie: Computational Risk Management
• Zusatzinfo: XI, 102 p. 54 illus., 48 illus. in color.
• Verlagsort: Singapore
• Sprache: englisch
• Themenwelt: Informatik ► Datenbanken ► Data Warehouse / Data Mining
• Themenwelt: Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management
• Schlagworte: autoregressive models • Business Analytics • Data Mining • Forecasting • GARCH • knowledge management • MATLAB • Open Source Software • predictive models • R software • Time Series
• ISBN-10: 981-10-2543-6 / 9811025436
• ISBN-13: 978-981-10-2543-3 / 9789811025433