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

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.