000 | 01380nam a22002057a 4500 | ||
---|---|---|---|
999 |
_c3031 _d3031 |
||
005 | 20220818143929.0 | ||
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 |