Wavelet Neural Networks

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: Determining a Networks Future Performance
Chapter 7 Modeling 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