Machine Learning in the Analysis and Forecasting of Financial Time Series
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.
Jaydip Sen is a Professor in Machine Learning and Artificial Intelligence at Praxis Business School, Kolkata, India. He is the author of more than 200 publications in the form of papers and book chapters, and has edited 18 volumes and co-authored four books. His active areas of work are applied statistical modeling, data mining and machine learning, social media analytics, artificial intelligence, and deep learning.
Sidra Mehtab received her MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology, India, in August 2020. Her research areas include econometrics, time series analysis, machine learning, and deep learning. She has published one journal article and two book chapters, and is the co-editor of two volumes.
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