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Books > Computing & IT > Applications of computing > Computer modelling & simulation
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Wavelet Neural Networks - With Applications in Financial Engineering, Chaos, and Classification (Hardcover)
Loot Price: R2,631
Discovery Miles 26 310
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Wavelet Neural Networks - With Applications in Financial Engineering, Chaos, and Classification (Hardcover)
Expected to ship within 10 - 15 working days
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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.
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