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 books 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.
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.
David L. Olson is the James & H.K. Stuart Chancellors 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 InslS2
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