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Machine Learning in the Analysis and Forecasting of Financial Time Series

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This book is a collection of real-world cases, illustrating how to handle challenging and volatile financial time series data for a better understanding of their past behavior and robust forecasting of their future movement. It demonstrates how the concepts and techniques of statistical, econometric, machine learning, and deep learning are applied to build robust predictive models, and the ways in which these models can be used for constructing profitable portfolios of investments. All the concepts and methods used here have been implemented using R and Python languages on TensorFlow and Keras frameworks. The book will be particularly useful for advanced postgraduate and doctoral students of finance, economics, econometrics, statistics, data science, computer science, and information technology.

Author(s): Jaydip Sen, Sidra Mehtab
Publisher: Cambridge Scholars Publishing
Year: 2022

Language: English
Pages: 384
City: Newcastle upon Tyne

Dedication
Table of Contents
List of Figures
List of Tables
Preface
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Contributors