Analysis and Forecasting of Financial Time Series: Selected Cases
This book brings together real-world cases illustrating how to analyse volatile financial time series in order to provide a better understanding of their past behavior and robust forecasting of their future behavioural patterns. Using time series data from diverse financial sectors, it shows how the concepts and techniques of statistical analysis, machine learning, and deep learning are applied to build robust predictive models, as well as the ways in which these models can be used for forecasting the future prices of stocks and constructing profitable portfolios of investments. All the concepts and methods used in the book have been implemented using Python and R languages on TensorFlow and Keras frameworks. The volume 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, India. He has authored around 250 papers and book chapters and four books, and has edited 20 further volumes. His areas of research are applied statistical modeling, data mining and machine learning, social media analytics, artificial intelligence, and deep learning.
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