金融数据建模的增量学习贝叶斯网络

Da Shi, Shaohua Tan
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引用次数: 4

摘要

发现金融变量之间的潜在关系将有力地支持各种金融研究。本文提出了一种新的贝叶斯网络增量学习算法,用于自动建立金融变量之间的关系。算法可以根据新生成的金融数据对学习结构进行部分更新,为算法的实时性提供了保证。实验结果表明,该算法在经典数据集和真实金融数据集上都优于所有可用的增量学习算法,甚至优于一些广泛使用的贝叶斯网络批处理学习算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incremental Learning Bayesian Networks for Financial Data Modeling
Discovering underlying relationships among financial variables will strongly support various financial researches. In this paper, A novel incremental learning algorithm for Bayesian networks is proposed to build up the relationships among financial variables automatically. Our algorithm can partially update the learned structure according to the new generated financial data, which provide a realtime guarantee on our algorithm. Experiment results show that our algorithm outperforms all the available incremental learning algorithms, even some widely used batch learning algorithms for Bayesian networks both on classic data sets and real financial data sets.
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