Wavelet Neural Networks: With Applications In Financial Engineering, Chaos, And Classification

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.

Antonios K. Alexandridis, PhD, is Lecturer of Finance in the School of Mathematics, Statistics, and Actuarial Science at the University of Kent. Dr. Alexandridis’ research interests include financial derivative modeling, pricing and forecasting, machine learning, and neural and wavelet networks.

Achilleas D. Zapranis, PhD, is Associate Professor in the Department of Finance and Accounting at the University of Macedonia, where he is also Vice Rector of Economic Planning and Development. In addition, Dr. Zapranis is a member of the Board of Directors of Thessaloniki’s Innovation Zone.