000 01380nam a22002057a 4500
999 _c3031
_d3031
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008 220818b ||||| |||| 00| 0 eng d
020 _a9781527583245
082 _a510
_bSEN
245 _aMachine learning in the analysis and forecasting of financial time series
260 _bCambridge Scholars
_aUK
_c2022
300 _axxi, 362 p.
365 _aGBP
_b75.99
520 _aThis 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.
650 _aFinancial Time Series
_98393
650 _aMachine Learning
_92343
700 _aSen, Jaydip
_96927
700 _aMehtab, Sidra
_98394
942 _2ddc
_cBK