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

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  • Format: Hardcover
  • Copyright: 5/5/2014
  • Publisher: Wiley
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Supplemental Materials

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Through extensive examples and case studies, Wavelet Neural Networks provides a step-by-step introduction to modeling, training, and forecasting using wavelet networks. The acclaimed authors present a statistical model identification framework to successfully apply wavelet networks in various applications, specifically, providing the mathematical and statistical framework needed for model selection, variable selection, wavelet network construction, initialization, training, forecasting and prediction, confidence intervals, prediction intervals, and model adequacy testing. The text is ideal for MBA students as well as researchers and practitioners. Various methodologies are separated into the appropriate procedural stages to facilitate understanding.

Table of Contents


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


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