| 书目名称 | Data Science for Financial Econometrics | | 编辑 | Nguyen Ngoc Thach,Vladik Kreinovich,Nguyen Duc Tru | | 视频video | | | 概述 | Presents recent findings and ideas on applying data science techniques to economic phenomena – and, in particular, financial phenomena.Inspires practitioners to learn how to apply various data science | | 丛书名称 | Studies in Computational Intelligence | | 图书封面 |  | | 描述 | .This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques. . | | 出版日期 | Book 2021 | | 关键词 | Computational Intelligence; Intelligent Systems; Econometrics; Data Science; Probabilistic Methods; Econo | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-48853-6 | | isbn_softcover | 978-3-030-48855-0 | | isbn_ebook | 978-3-030-48853-6Series ISSN 1860-949X Series E-ISSN 1860-9503 | | issn_series | 1860-949X | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 17:47:19
