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

Book Description

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.

    Table of Contents

    1. Preface
    2. Chapter 1: Machine Learning and Financial Engineering
      1. Financial Engineering
      2. Financial Engineering and Related Research Areas
      3. Functions of Financial Engineering
      4. Applications of Machine Learning in Finance
      5. From Neural to Wavelet Networks
      6. Applications of Wavelet Neural Networks in Financial Engineering, Chaos, and Classification
      7. Building Wavelet Networks
      8. Book Outline
      9. References
    3. Chapter 2: Neural Networks
      1. Parallel Processing
      2. Perceptron
      3. The Delta Rule
      4. Backpropagation Neural Networks
      5. The Generalized Delta Rule
      6. Backpropagation in practice
      7. Training with Backpropagation
      8. Configuration Reference
      9. Conclusions
      10. References
    4. Chapter 3: Wavelet Neural Networks
      1. Wavelet Neural Networks for Multivariate Process Modeling
      2. Conclusions
      3. References
    5. Chapter 4: Model Selection: Selecting the Architecture of the Network
      1. The Usual Practice
      2. Minimum Prediction Risk
      3. Estimating the Prediction Risk Using Information Criteria
      4. Estimating the Prediction Risk Using Sampling Techniques
      5. Evaluating the Model Selection Algorithm
      6. Adaptive Networks and Online synthesis
      7. Conclusions
      8. References
    6. Chapter 5: Variable Selection: Determining the Explanatory Variables
      1. Existing Algorithms
      2. Sensitivity Criteria
      3. Model Fitness Criteria
      4. Algorithm for Selecting the Significant Variables
      5. Evaluating the Variable Significance Criteria
      6. Conclusions
      7. References
    7. Chapter 6: Model Adequacy: Determining a Network's Future Performance
      1. Testing the residuals
      2. Evaluation criteria for the prediction ability of the wavelet network
      3. Two simulated Cases
      4. Classification
      5. Conclusions
      6. References
    8. Chapter 7: Modeling Uncertainty: From Point Estimates to Prediction Intervals
      1. The Usual Practice
      2. Confidence and Prediction Intervals
      3. Constructing Confidence Intervals
      4. Constructing Prediction Intervals
      5. Evaluating the Methods for Constructing Confidence and Prediction Intervals
      6. Conclusions
      7. References
    9. Chapter 8: Modeling Financial Temperature Derivatives
      1. Weather Derivatives
      2. Data Description and Preprocessing
      3. Data Examination
      4. Model for the daily average temperature: Gaussian Ornstein–Uhlenbeck process with lags and time-varying mean reversion
      5. Estimation Using Wavelet Networks
      6. Conclusions
      7. References
    10. Chapter 9: Modeling Financial Wind Derivatives
      1. Modeling the Daily Average Wind Speed
      2. Linear ARMA Model
      3. Wavelet Networks for Wind Speed Modeling
      4. Forecasting Daily Average Wind Speeds
      5. Conclusions
      6. References
    11. Chapter 10: Predicting Chaotic Time Series
      1. Mackey–Glass Equation
      2. Model Selection
      3. Initialization and training
      4. Model Adequacy
      5. Predicting the evolution of the chaotic Mackey–Glass time series
      6. Confidence and prediction intervals
      7. Conclusions
      8. References
    12. Chapter 11: Classification of Breast Cancer Cases
      1. Part A: Classification of Breast Cancer
      2. Part B: Cross-validation in Breast Cancer Classification in Wisconsin
      3. Part C: Classification of Breast Cancer (Continued)
      4. Conclusions
      5. References
    13. Index
    14. End User License Agreement