Artificial Higher Order Neural Networks for Economics and Business

This book is the first book to provide opportunities for millions working in economics, accounting, finance and other business areas education on HONNs, the ease of their usage, and directions on how to obtain more accurate application results. It provides significant, informative advancements in the subject and introduces the HONN group models and adaptive HONNs"--Provided by publisher.

ISBN	1599048973	Pages	365
ISBN13	9781599048970 (What's this?)	Volumes	1
Publisher	IGI Global	Weight (grammes)	1542
Imprint	Information Science Reference	Published in	Hershey
Format	Hardback	Series title	Premier Reference Source
Publication date	15 Sep 2008	Height (mm)	279
Library of Congress	2007043953	Width (mm)	221
DEWEY	332.0285632	Spine width (mm)	33
DEWEY edition	DC22	Academic level	Professional / Scholarly

Sect. I	Artificial Higher Order Neural Networks for Economics
Ch. I	Artificial Higher Order Neural Network Nonlinear Models: SAS NLIN or HONNs? by Ming Zhang	1
Ch. II	Higher Order Neural Networks with Bayesian Confidence Measure for the Prediction of the EUR/USD Exchange Rate by Adam Knowles and Abir Hussain and Wael El Deredy and Paulo G. J. Lisboa and Christian L. Dunis	48
Ch. III	Automatically Identifying Predictor Variables for Stock Return Prediction by Do Shi and Shaohua Tan and Shuzhi Sam Ge	60
Ch. IV	Higher Order Neural Network Architectures for Agent-Based Computational Economics and Finance by John Seiffertt and Donald C. Wunsch II	79
Ch. V	Foreign Exchange Rate Forecasting Using Higher Order Flexible Neural Tree by Yuehui Chen and Peng Wu and Qiang Wu	94
Ch. VI	Higher Order Neural Networks for Stock Index Modeling by Yuehui Chen and Peng Wu and Qiang Wu	113
Sect. II	Artificial Higher Order Neural Networks for Time Series Data
Ch. VII	Ultra High Frequency Trigonometric Higher Order Neural Networks for Time Series Data Analysis by Ming Zhang	133
Ch. VIII	Artificial Higher Order Pipeline Recurrent Neural Networks for Financial Time Series Prediction by Panos Liatsis and Abir Hussain and Efstathios Milonidis	164
Ch. IX	A Novel Recurrent Polynomial Neural Network for Financial Time Series Prediction by Abir Hussain and Panos Liatsis	190
Ch. X	Generalized Correlation Higher Order Neural Networks for Financial Time Series Prediction by David R. Selviah and Janti Shawash	212
Ch. XI	Artificial Higher Order Neural Networks in Time Series Prediction by Godfrey C. Onwubolu	250
Ch. XII	Application of Pi-Sigma Neural Networks and Ridge Polynomial Neural Networks to Financial Time Series Prediction by Rozaida Ghazali and Dhiya Al-Jumeily	271
Sect. III	Artificial Higher Order Neural Networks for Business
Ch. XIII	Electric Load Demand and Electricity Prices Forecasting Using Higher Order Neural Networks Trained by Kalman Filtering by Edgar N. Sanchez and Alma Y. Alanis and Jesus Rico	295
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Artificial Higher Order Neural Networks for Economics and Business

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