Neural Networks in Business Forecasting

by Zhang, G. Peter

• ISBN13: 9781591401766
• ISBN10: 1591401763
• Format: Hardcover
• Copyright: 2003-10-01
• Publisher: Igi Global

Summary

Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. There are considerable interests and applications in forecasting using neural networks. Neural Networks in Business Forecasting provides for researchers and practitioners some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.

Table of Contents

Preface	vi
G. Peter Zhang, Georgia State University, USA
Chapter I. Business Forecasting with Artificial Neural Networks: An Overview	1	(22)
G. Peter Zhang, Georgia State University, USA
Chapter II. Using Artificial Neural Networks to Forecast Market Response	23	(18)
Leonard J. Parsons, Georgia Institute of Technology, USA
Ashutosh Dixit, University of Georgia, USA
Chapter III. Forecasting Stock Returns with Artificial Neural Networks	41	(39)
Suraphan Thawornwong, Thailand Securities Depository, Thailand
David Enke, University of Missouri - Rolla, USA
Chapter IV. Forecasting Emerging Market Indexes with Neural Networks	80	(22)
Steven Walczak, University of Colorado at Denver, USA
Chapter V. Predicting Wastewater BOD Levels with Neural Network Time Series Models	102	(19)
David West, East Carolina University, USA
Scott Dellang, East Carolina University, USA
Chapter VI. Tourism Demand Forecasting for the Tourism Industry: A Neural Network Approach	121	(21)
Rob Law, The Hong Kong Polytechnic University, Hong Kong
Ray Pine, The Hong Kong Polytechnic University, Hong Kong
Chapter VII. Using an Extended Self-Organizing Map Network to Forecast Market Segment Membership	142	(16)
Melody Y. Kiang, California State University, Long Beach, USA
Dorothy M. Fisher, California State University, Dominguez Hills, USA
Michael Y. Hu, Kent State University, USA
Robert T. Chi, California State University, Long Beach, USA
Chapter VIII. Backpropagation and Kohonen Self-Organizing Feature Map in Bankruptcy Prediction	158	(14)
Kidong Lee, University of Incheon, South Korea
David Booth, Kent State University, USA
Pervaiz Alam, Kent State University, USA
Chapter IX. Predicting Consumer Situational Choice with Neural Networks	172	(23)
Michael Y. Hu, Kent State University, USA
Murali Shanker, Kent State University, USA
Ming S. Hung, Optimal Solutions Technologies, Inc., USA
Chapter X. Forecasting Short-Term Exchange Rates: A Recurrent Neural Network Approach	195	(18)
Leong-Kwan Li, The Hong Kong Polytechnic University, Hong Kong
Wan-Kai Pang, The Hong Kong Polytechnic University, Hong Kong
Wing-Tong Yu, The Hong Kong Polytechnic University, Hong Kong
Marvin D. Troutt, Kent State University, USA
Chapter XI. A Combined ARIMA and Neural Network Approach for Time Series Forecasting	213	(13)
G. Peter Zhang, Georgia State University, USA
Chapter XII. Methods for Multi-Step Time Series Forecasting with Neural Networks	226	(25)
Douglas M. Kline, University of North Carolina at Wilmington, USA
Chapter XIII. A Weighted Window Approach to Neural Network Time Series Forecasting	251	(15)
Bradley H. Morantz, Georgia State University, USA
Thomas Whalen, Georgia State University, USA
G. Peter Zhang, Georgia State University, USA
Chapter XIV. Assessment of Evaluation Methods for Prediction and Classifications of Consumer Risk in the Credit Industry	266	(19)
Satish Nargundkar, Georgia State University, USA
Jennifer Lewis Priestley, Georgia State University, USA
About the Authors	285	(8)
Index	293