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

Antonios K Alexandridis

ISBN 10: 1118592522 / ISBN 13: 9781118592526

Editore: Wiley Mai 2014, 2014

Riguardo questo articolo

Neuware - 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.

Codice articolo 9781118592526

Dati bibliografici

• Titolo: Wavelet Neural Networks : With Applications in Financial Engineering, Chaos, and Classification
• Editore: Wiley Mai 2014
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 1118592522
• ISBN 13: 9781118592526
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 240 L x 161 A x 19 P (millimetri) 240 millimetri di larghezza per 161 millimetri di altezza per 19 millimetri di profondità
• Peso: 563 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Antonios K. Alexandridis, PhD, is Lecturer of Finance in the School of Mathematics, Statistics, and Actuarial Science at the University of Kent. Dr. Alexandridis’ research interests include financial derivative modeling, pricing and forecasting, machine learning, and neural and wavelet networks.

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