用增量域知识对金融数据进行视觉探索
Alessio Arleo1,2, Christos Tsigkanos1,3, Roger A Leite1,2
1TU Wien Vienna Austria.
概括
萨布里娜2.0是一种视觉分析 (VA) 方法,它集成了各种金融数据,创建了公司对公司的交易网络. 这有助于理解复杂的经济关系和国家经济.
科学领域:
- 金融数据分析 金融数据分析
- 视觉分析 视觉分析 视觉分析
- 经济建模经济建模
背景情况:
- 金融环境是复杂的,需要整合不同的数据,以获得全面的理解.
- 在不同规模上分散的信息阻碍了对公司关系和经济景观的理解.
研究的目的:
- 为了介绍Sabrina 2.0,一种用于探索财务数据的视觉分析 (VA) 方法.
- 开发一个管道来生成公司对公司的金融交易网络.
- 通过整合跨规模的数据,促进对国家经济的整体理解.
主要方法:
- 开发了Sabrina 2.0,这是一个用于金融数据探索的视觉分析 (VA) 解决方案.
- 创建了一个管道,通过合并公司级数据,部门交易和经济领域知识来生成公司对公司的金融交易网络.
- 启用多实例网络生成以进行场景比较.
主要成果:
- 萨布里娜2.0促进了对金融数据的洞察力.
- 该方法成功地将个人企业的信息整合到全国范围的聚合物中.
- 纳入交易模型可以增强用户对国家经济的探索.
结论:
- 萨布里娜2.0提供了一个强大的VA方法来分析复杂的金融环境.
- 该系统有效地将微观 (企业级) 和宏观 (国家级) 经济数据相结合.
- 专家评估证实了Sabrina 2.0在产生经济见解方面的实用性.
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