Machine learning in the analysis and forecasting of financial time series
Material type:
TextPublication details: UK Cambridge Scholars 2022 Description: xxi, 362 pISBN: 9781527583245Subject(s): Financial Time Series | Machine LearningDDC classification: 510 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.
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 510 SEN (Browse shelf(Opens below)) | 1 | Checked out | 10/12/2024 | 002998 |
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| 384.34 NEW World without email: | 384.38 EVE The YouTube formula: how anyone can unlock the algorithm to drive views, build an audience, and grow revenue | 401.410285555 BEC Textual data science with R | 510 SEN Machine learning in the analysis and forecasting of financial time series | 510.285536 TUR Practical MATLAB: | 511.8 NIE Quantum computation and quantum information | 518.1 JAT Constitution of algorithms: |
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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