This book provides fundamental concepts and algorithms of the Hidden Markov Model (HMM) and its applications in finance, such as stock price predictions, and other areas such as speech recognition. Their wide range uses make HMMs very attractive to researchers in both academia and industry.
This book provides fundamental concepts and algorithms of the Hidden Markov Model (HMM) and its applications in finance, such as stock price predictions, and other areas such as speech recognition. Their wide range uses make HMMs very attractive to researchers in both academia and industry. Only a basic knowledge of probability, statistics, and programming is necessary, and readers will learn the concepts and algorithms of the HMM through definitions, real-life examples, and R code.
Key Features:
• A comprehensive introduction to the concepts and algorithms of Hidden Markov Models (HMMs).
• Real-world examples that can be worked through using a calculator or R.
• Applications across disciplines, including finance, bioinformatics, and speech recognition.
• Fully annotated R code for hands-on learning and practical implementation.
Preface Author 1 Introduction 2 Three Main Problems of The HMM and Its
Algorithms 3 Model Selections 4 Applications of HMM in Finance 5 Applications
of HMM in other disciplines Bibliography Index
Dr. Nguyet (Moon) Nguyen is an Associate Professor of Mathematics at Youngstown State University. She earned her M.S. and Ph.D. in Financial Mathematics from Florida State University and specializes in predictive modeling, data analysis, and quantitative finance. With over a decade of research on Hidden Markov Models, she has published widely on their applications in stock forecasting, economic modeling, and portfolio management, and collaborates actively with industry partners.
Lisainfo e-raamatute kohta