Predictive Data Mining Models

  • David L. Olson
  • Desheng Wu

Part of the Computational Risk Management book series (Comp. Risk Mgmt)

Table of contents

  1. Front Matter
    Pages i-xi
  2. David L. Olson, Desheng Wu
    Pages 1-7
  3. David L. Olson, Desheng Wu
    Pages 9-15
  4. David L. Olson, Desheng Wu
    Pages 17-33
  5. David L. Olson, Desheng Wu
    Pages 35-43
  6. David L. Olson, Desheng Wu
    Pages 45-54
  7. David L. Olson, Desheng Wu
    Pages 55-69
  8. David L. Olson, Desheng Wu
    Pages 71-93
  9. David L. Olson, Desheng Wu
    Pages 95-97
  10. Back Matter
    Pages 99-102

About this book

Introduction

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.

Keywords

Business Analytics Data Mining Time Series Forecasting Open Source Software Knowledge Management Predictive Models Autoregressive Models GARCH R Software Matlab

Authors and affiliations

  • David L. Olson
    • 1
  • Desheng Wu
    • 2
  1. 1.College of BusinessUniversity of Nebraska College of BusinessLincolnUSA
  2. 2.Econonics and Management SchoolUniv. of Chinese Academy of Sciences Econonics and Management SchoolBeijingChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-10-2543-3
  • Copyright Information Springer Science+Business Media Singapore 2017
  • Publisher Name Springer, Singapore
  • eBook Packages Business and Management
  • Print ISBN 978-981-10-2542-6
  • Online ISBN 978-981-10-2543-3
  • Series Print ISSN 2191-1436
  • Series Online ISSN 2191-1444
  • About this book