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Neural Networks in Business Forecasting
 
 
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Neural Networks in Business Forecasting [Hardcover]

G. Peter Zhang (Author, Editor)

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Book Description

July 1, 2003
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.

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About the Author

Dr. Zhang is currently Associate Professor of Decision Sciences and Operations Management at Georgia State University. He received his B.S. and M.S. degrees in Mathematics and Statistics, respectively, from East China Normal University, Shanghai, China, and Ph.D. degree in Operations Research/Operations Management from Kent State University, USA. His research interests include neural networks, time series forecasting, supply chain management, and statistical quality control. His work has appeared in several reputable publications. He is on the editorial review board of Production and Operations Management journal. His profile has been published in Who’s Who in the 21st Century and Who’s Who in the World.

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Inside This Book (learn more)
First Sentence:
The recent upsurge in research activities into artificial neural networks (ANNs) has proven that neural networks have powerful pattern classification and prediction capabilities. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
weighted window approach, network forecasting model, number ofhidden nodes, stock return forecasting, forecasting with artificial neural networks, global classification rate, neural network time series model, multistep forecasting, tourism demand forecasting, market response models, time series forecasting problems, sales response function, ofneural networks, forecasting using neural networks, market index values, neural network forecasting, tourism forecasting, winning frequency, forecasting neural networks, moving window approach, joint method, lagged observations, underlying data generating process, time series structure, market share models
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Hong Kong, New York, Operations Research, John Wiley, Hang Seng, Kent State University, Pacific Rim, Kluwer Academic, Georgia State University, Neural Computation, Decision Sciences, Economic Statistics, Journal of Business, Annals of Tourism Research, Journal of Travel, European Journal of Operational Research, Journal of Marketing Research, Months Since Most Recent, Trades Currently Rated, Bank Revolving Trades Ever, Cambridge University Press, German Mark, International Conference, Morgan Kaufmann, Pacific Basin Financial Markets
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