Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification

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9781118592526: Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification
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A step-by-step introduction to modeling, training, andforecasting using wavelet networks

Wavelet Neural Networks: With Applications in FinancialEngineering, Chaos, and Classification presents the statisticalmodel identification framework that is needed to successfully applywavelet networks as well as extensive comparisons of alternatemethods. Providing a concise and rigorous treatment forconstructing optimal wavelet networks, the book links mathematicalaspects of wavelet network construction to statistical modeling andforecasting applications in areas such as finance, chaos, andclassification.

The authors ensure that readers obtain a complete understandingof model identification by providing in-depth coverage of bothmodel selection and variable significance testing. Featuring anaccessible approach with introductory coverage of the basicprinciples of wavelet analysis, Wavelet Neural Networks: WithApplications in Financial Engineering, Chaos, andClassification also includes:

· Methods that can be easily implemented or adapted byresearchers, academics, and professionals in identification andmodeling for complex nonlinear systems and artificialintelligence

· Multiple examples and thoroughly explained procedureswith numerous applications ranging from financial modeling andfinancial engineering, time series prediction and construction ofconfidence and prediction intervals, and classification and chaotictime series prediction

· An extensive introduction to neural networks that beginswith regression models and builds to more complex frameworks

· Coverage of both the variable selection algorithm andthe model selection algorithm for wavelet networks in addition tomethods for constructing confidence and prediction intervals

Ideal as a textbook for MBA and graduate-level courses inapplied neural network modeling, artificial intelligence, advanceddata analysis, time series, and forecasting in financialengineering, the book is also useful as a supplement for courses ininformatics, identification and modeling for complex nonlinearsystems, and computational finance. In addition, the book serves asa valuable reference for researchers and practitioners in thefields of mathematical modeling, engineering, artificialintelligence, decision science, neural networks, and finance andeconomics.

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About the Author:

Antonios K. Alexandridis, PhD, is Lecturer of Finance inthe School of Mathematics, Statistics, and Actuarial Science at theUniversity of Kent. Dr. Alexandridis’ research interestsinclude financial derivative modeling, pricing and forecasting,machine learning, and neural and wavelet networks.

Achilleas D. Zapranis, PhD, is Associate Professor in theDepartment of Finance and Accounting at the University ofMacedonia, where he is also Vice Rector of Economic Planning andDevelopment. In addition, Dr. Zapranis is a member of the Board ofDirectors of Thessaloniki’s Innovation Zone.

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Book Description John Wiley & Sons Inc, United States, 2014. Hardback. Condition: New. 1. Auflage. Language: English. Brand new Book. A step-by-step introduction to modeling, training, and forecasting using wavelet networks Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification presents the statistical model identification framework that is needed to successfully apply wavelet networks as well as extensive comparisons of alternate methods. Providing a concise and rigorous treatment for constructing optimal wavelet networks, the book links mathematical aspects of wavelet network construction to statistical modeling and forecasting applications in areas such as finance, chaos, and classification. The authors ensure that readers obtain a complete understanding of model identification by providing in-depth coverage of both model selection and variable significance testing. Featuring an accessible approach with introductory coverage of the basic principles of wavelet analysis, Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification also includes: Methods that can be easily implemented or adapted by researchers, academics, and professionals in identification and modeling for complex nonlinear systems and artificial intelligence Multiple examples and thoroughly explained procedures with numerous applications ranging from financial modeling and financial engineering, time series prediction and construction of confidence and prediction intervals, and classification and chaotic time series prediction An extensive introduction to neural networks that begins with regression models and builds to more complex frameworks Coverage of both the variable selection algorithm and the model selection algorithm for wavelet networks in addition to methods for constructing confidence and prediction intervals Ideal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial intelligence, advanced data analysis, time series, and forecasting in financial engineering, the book is also useful as a supplement for courses in informatics, identification and modeling for complex nonlinear systems, and computational finance. In addition, the book serves as a valuable reference for researchers and practitioners in the fields of mathematical modeling, engineering, artificial intelligence, decision science, neural networks, and finance and economics. Seller Inventory # AAH9781118592526

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Book Description John Wiley Sons Inc, United States, 2014. Hardback. Condition: New. 1. Auflage. Language: English . Brand New Book. A step-by-step introduction to modeling, training, and forecasting using wavelet networks Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification presents the statistical model identification framework that is needed to successfully apply wavelet networks as well as extensive comparisons of alternate methods. Providing a concise and rigorous treatment for constructing optimal wavelet networks, the book links mathematical aspects of wavelet network construction to statistical modeling and forecasting applications in areas such as finance, chaos, and classification. The authors ensure that readers obtain a complete understanding of model identification by providing in-depth coverage of both model selection and variable significance testing. Featuring an accessible approach with introductory coverage of the basic principles of wavelet analysis, Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification also includes: Methods that can be easily implemented or adapted by researchers, academics, and professionals in identification and modeling for complex nonlinear systems and artificial intelligence Multiple examples and thoroughly explained procedures with numerous applications ranging from financial modeling and financial engineering, time series prediction and construction of confidence and prediction intervals, and classification and chaotic time series prediction An extensive introduction to neural networks that begins with regression models and builds to more complex frameworks Coverage of both the variable selection algorithm and the model selection algorithm for wavelet networks in addition to methods for constructing confidence and prediction intervals Ideal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial intelligence, advanced data analysis, time series, and forecasting in financial engineering, the book is also useful as a supplement for courses in informatics, identification and modeling for complex nonlinear systems, and computational finance. In addition, the book serves as a valuable reference for researchers and practitioners in the fields of mathematical modeling, engineering, artificial intelligence, decision science, neural networks, and finance and economics. Seller Inventory # AAH9781118592526

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Book Description John Wiley & Sons Inc, United States, 2014. Hardback. Condition: New. 1. Auflage. Language: English. Brand new Book. A step-by-step introduction to modeling, training, and forecasting using wavelet networks Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification presents the statistical model identification framework that is needed to successfully apply wavelet networks as well as extensive comparisons of alternate methods. Providing a concise and rigorous treatment for constructing optimal wavelet networks, the book links mathematical aspects of wavelet network construction to statistical modeling and forecasting applications in areas such as finance, chaos, and classification. The authors ensure that readers obtain a complete understanding of model identification by providing in-depth coverage of both model selection and variable significance testing. Featuring an accessible approach with introductory coverage of the basic principles of wavelet analysis, Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification also includes: Methods that can be easily implemented or adapted by researchers, academics, and professionals in identification and modeling for complex nonlinear systems and artificial intelligence Multiple examples and thoroughly explained procedures with numerous applications ranging from financial modeling and financial engineering, time series prediction and construction of confidence and prediction intervals, and classification and chaotic time series prediction An extensive introduction to neural networks that begins with regression models and builds to more complex frameworks Coverage of both the variable selection algorithm and the model selection algorithm for wavelet networks in addition to methods for constructing confidence and prediction intervals Ideal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial intelligence, advanced data analysis, time series, and forecasting in financial engineering, the book is also useful as a supplement for courses in informatics, identification and modeling for complex nonlinear systems, and computational finance. In addition, the book serves as a valuable reference for researchers and practitioners in the fields of mathematical modeling, engineering, artificial intelligence, decision science, neural networks, and finance and economics. Seller Inventory # BTE9781118592526

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Book Description Wiley. Hardcover. Condition: New. 264 pages. Dimensions: 9.3in. x 6.1in. x 0.8in.A step-by-step introduction to modeling, training, and forecasting using wavelet networksWavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification presents the statistical model identification framework that is needed to successfully apply wavelet networks as well as extensive comparisons of alternate methods. Providing a concise and rigorous treatment for constructing optimal wavelet networks, the book links mathematical aspects of wavelet network construction to statistical modeling and forecasting applications in areas such as finance, chaos, and classification. The authors ensure that readers obtain a complete understanding of model identification by providing in-depth coverage of both model selection and variable significance testing. Featuring an accessible approach with introductory coverage of the basic principles of wavelet analysis, Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification also includes: Methods that can be easily implemented or adapted by researchers, academics, and professionals in identification and modeling for complex nonlinear systems and artificial intelligence Multiple examples and thoroughly explained procedures with numerous applications ranging from financial modeling and financial engineering, time series prediction and construction of confidence and prediction intervals, and classification and chaotic time series prediction An extensive introduction to neural networks that begins with regression models and builds to more complex frameworks Coverage of both the variable selection algorithm and the model selection algorithm for wavelet networks in addition to methods for constructing confidence and prediction intervalsIdeal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial intelligence, advanced data analysis, time series, and forecasting in financial engineering, the book is also useful as a supplement for courses in informatics, identification and modeling for complex nonlinear systems, and computational finance. In addition, the book serves as a valuable reference for researchers and practitioners in the fields of mathematical modeling, engineering, artificial intelligence, decision science, neural networks, and finance and economics. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Hardcover. Seller Inventory # 9781118592526

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