Wavelet Neural Networks (PDF)

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.