Details
Predictive Data Mining Models
Computational Risk Management
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96,29 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 26.09.2016 |
| ISBN/EAN: | 9789811025433 |
| Sprache: | englisch |
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Beschreibungen
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.
<div>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.</div><div><br></div>
<div>David L. Olson is the James & H.K. Stuart Professor and Chancellor’s Professor at the University of Nebraska, USA. He has published research in over 200 refereed journal articles and has authored over 40 books, including Decision Aids for Selection Problems, Introduction to Information Systems Project Management, Managerial Issues of Enterprise Resource Planning Systems, Supply Chain Risk Management, and Supply Chain Information Technology. He has served as associate editor of Service Business, Decision Support Systems, and Decision Sciences and co-editor in chief of International Journal of Services Sciences. He 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 has named the Raymond E. Miles DistinguishedScholar award for 2002 and was a James C. and Rhonda Seacrest Fellow from 2005 to 2006. He was named Best Enterprise Information Systems Educator by IFIP in 2006. He is a Fellow of the Decision Sciences Institute.<br></div><div><br></div><div><div>Desheng Wu is a Special-term Professor at University of Chinese Academy of Sciences, Beijing, China, and a Professor at Stockholm University, Sweden. He has published over 150 ISI-indexed papers in refereed journals, such as Production and Operations Management, Decision Sciences, Risk Analysis, and IEEE Transactions on Systems, Man, and Cybernetics, as well as 7 books with publishers like Springer. He is an elected member of Academia Europaea (The Academy of Europe), the European Academy of Sciences and Arts, and the International Eurasian Academy of Sciences. He has served asan associate editor and a guest editor for several journals, such as Risk Analysis, IEEE Transactions on Systems, Man, and Cybernetics, the Annals of Operations Research, Computers and Operations Research, the International Journal of Production Economics, and Omega. He is the editor of Springer’s book series on computational risk management.</div><div><b><br></b></div><div><b> </b></div></div><div><br></div>
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.
Includes detailed discussions of time series data and different characteristics that data may have Describes data mining processes used in predictive modeling Includes demonstrations of modeling with R and with Matlab Includes supplementary material: sn.pub/extras
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