Machine learning in the analysis and forecasting of financial time series / Jaydip Sen, Sidra Mehtab.

Format:

Book

Author/Creator:

Sen, Jaydip, author.

Mehtab, Sidra, author.

Language:

English

Subjects (All):

Artificial intelligence--Financial applications.

Artificial intelligence.

Artificial intelligence--History.

Physical Description:

1 online resource (384 pages)

Place of Publication:

Newcastle upon Tyne : Cambridge Scholars Publishing, [2022]

Summary:

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.

Contents:

Intro

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.

Notes:

Description based on print version record.

Other Format:

Print version: Sen, Jaydip Machine Learning in the Analysis and Forecasting of Financial Time Series

ISBN:

1-5275-8325-2