An Application Of The Feedforward Neural Network Model In Currency Exchange Rate Forecasting
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An Application Of The Feedforward Neural Network Model In Currency Exchange Rate Forecasting
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Author : Julia X. Ye
language : en
Publisher:
Release Date : 1994
An Application Of The Feedforward Neural Network Model In Currency Exchange Rate Forecasting written by Julia X. Ye and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Foreign exchange rates categories.
Foreign Exchange Rate Forecasting With Artificial Neural Networks
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Author : Lean Yu
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-02-26
Foreign Exchange Rate Forecasting With Artificial Neural Networks written by Lean Yu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-02-26 with Business & Economics categories.
This book focuses on forecasting foreign exchange rates via artificial neural networks (ANNs), creating and applying the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange rate forecasting. The result is an up-to-date review of the most recent research developments in forecasting foreign exchange rates coupled with a highly useful methodological approach to predicting rate changes in foreign currency exchanges.
Neural Networks In Business Forecasting
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Author : G. Peter Zhang
language : en
Publisher: IGI Global
Release Date : 2004-01-01
Neural Networks In Business Forecasting written by G. Peter Zhang and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-01-01 with Computers categories.
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. This book 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.
Exchange Rate Forecasting
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Author : Yih-Jiuan Wu
language : en
Publisher:
Release Date : 1998
Exchange Rate Forecasting written by Yih-Jiuan Wu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
The purpose of this research is to investigate the forecasting performance of Artificial Neural Network models applied to foreign exchange rates. The study concentrates on the behavior of forecasts of exchange rates generated from the radial basis function (RBF) network models where little previous work exists. Exchange rates examined are the German mark/US dollar, Japanese yen/US dollar, and Italian lira/US dollar. One-step-ahead forecasts from univariate and multivariate RBF models are compared with those generated from ARIMA models, random walk forecasts and the forward rates. Interest rates and the money supply (MI) are used as explanatory variables in the multivariate analyses. Out-of-sample evaluation criteria include root mean squared error, "correct direction", and "speculative direction."
Neural Networks For Identification Prediction And Control
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Author : Duc T. Pham
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Neural Networks For Identification Prediction And Control written by Duc T. Pham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense. Their use enables the behaviour of complex systems to be modelled and predicted and accurate control to be achieved through training, without a priori information about the systems' structures or parameters. This book describes examples of applications of neural networks In modelling, prediction and control. The topics covered include identification of general linear and non-linear processes, forecasting of river levels, stock market prices and currency exchange rates, and control of a time-delayed plant and a two-joint robot. These applications employ the major types of neural networks and learning algorithms. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network. In addition, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems are also presented. The main learning algorithm adopted in the applications is the standard backpropagation (BP) algorithm. Widrow-Hoff learning, dynamic BP and evolutionary learning are also described.
Neural Networks In Business
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Author : Kate A. Smith
language : en
Publisher: IGI Global
Release Date : 2003-01-01
Neural Networks In Business written by Kate A. Smith and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-01-01 with Computers categories.
"For professionals, students, and academics interested in applying neural networks to a variety of business applications, this reference book introduces the three most common neural network models and how they work. A wide range of business applications and a series of global case studies are presented to illustrate the neural network models provided. Each model or technique is discussed in detail and used to solve a business problem such as managing direct marketing, calculating foreign exchange rates, and improving cash flow forecasting."
Forecasting Foreign Exchange Rate Movements With K Nearest Neighbor Ridge Regression And Feed Forward Neural Networks
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Author : Milan Fičura
language : en
Publisher:
Release Date : 2019
Forecasting Foreign Exchange Rate Movements With K Nearest Neighbor Ridge Regression And Feed Forward Neural Networks written by Milan Fičura and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Forecasting Exchange Rates Using Feedforward And Recurrent Neural Networks
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Author : Chongming Guan
language : en
Publisher:
Release Date : 1994
Forecasting Exchange Rates Using Feedforward And Recurrent Neural Networks written by Chongming Guan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with categories.
Forecasting Foreign Exchange Rate Movements With K Nearest Neighbour Ridge Regression And Feed Forward Neural Networks
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Author : Milan Fičura
language : en
Publisher:
Release Date : 2017
Forecasting Foreign Exchange Rate Movements With K Nearest Neighbour Ridge Regression And Feed Forward Neural Networks written by Milan Fičura and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
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Forecasting Exchange Rates Using General Regression Neural Networks
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Author : Mark T. Leung
language : en
Publisher:
Release Date : 2004
Forecasting Exchange Rates Using General Regression Neural Networks written by Mark T. Leung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
In this study, we examine the forecastability of a specific neural network architecture called General Regression Neural Network (GRNN) and compare its performance with a variety of forecasting techniques, including Multi-Layered Feedforward Network (MLFN), multivariate transfer function, and random walk models. The comparison with MLFN provides a measure of GRNN's performance relative to the more conventional type of neural networks while the comparison with transfer function models examines the difference in predictive strength between the non-parametric and parametric techniques. The random walk model is used for benchmark comparison. Our findings show that GRNN not only has a higher degree of forecasting accuracy but also performs statistically better than other evaluated models for different currencies.