Alexandridis / Zapranis Wavelet Neural Networks

With Applications in Financial Engineering, Chaos, and Classification
1. Auflage 2014
ISBN: 978-1-118-59629-6
Verlag: John Wiley & Sons
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 264 Seiten, E-Book

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.

Autoren/Hrsg.

Alexandridis, Antonios K.

Zapranis, Achilleas D.

Inhaltsverzeichnis

Preface
Chapter 1: Machine Learning and Financial Engineering
Chapter 2: Neural Networks
Chapter 3: Wavelet Neural Networks
Chapter 4: Model Selection: Selecting the Architecture of the Network
Chapter 5: Variable Selection: Determining the Explanatory Variables
Chapter 6: Model Adequacy Testing: Determining the Networks Future Performance
Chapter 7: Modeling the Uncertainty: From Point Estimates to Prediction Intervals
Chapter 8: Modeling Financial Temperature Derivatives
Chapter 9: Modeling Financial Wind Derivatives
Chapter 10: Predicting Chaotic Time Series
Chapter 11: Classification of Breast Cancer Cases
Index

Über Autor(innen)

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.