Machine learning in the analysis and forecasting of financial time series

Contributor(s):

Material type: Text

TextPublication details: Cambridge Scholars UK 2022Description: xxi, 362 pISBN:

9781527583245

Subject(s):

DDC classification:

510 SEN

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.

List(s) this item appears in: IT & Decision Sciences | Hindi Books

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	510 SEN (Browse shelf(Opens below))	1	Checked out	10/12/2024	002998

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.

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