Coupled Behavior Analysis with Applications
Coupled Behavior Analysis with Applications
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DOI：
10.1109/tkde.2011.129
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发表时间：
2012-08-01
影响因子：
8.9
通讯作者：
Yu, Philip S.
中科院分区：
文献类型：
--
作者：
Cao, Longbing;Ou, Yuming;Yu, Philip S.
耦合行为是指在一定关系上相互关联的一对多行为者的活动。随着基于网络和社区的事件和应用的增加，例如基于群体的犯罪和社交网络交互，行为耦合会导致最终的业务问题。由于现有方法主要集中于个体行为分析，因此还没有有效的方法来分析耦合行为。本文讨论了耦合行为分析（CBA）的问题及其挑战。说明了基于耦合隐马尔可夫模型 (CHMM) 的方法来建模和检测基于群体的异常交易行为。 CHMM 模型适用于：1) 一群人的多种行为，2) 行为属性，3) 行为、客户和行为属性之间的交互，以及 4) 耦合行为之间的显着变化。我们在来自亚洲主要交易所的订单簿级别股票报价数据上演示和评估了模型，并证明所提出的 CHMM 在建模单个序列或组合多个单个序列时优于仅 HMM，而不考虑耦合关系来检测异常。最后，我们讨论了耦合行为之间的交互关系和模式，这些都是值得大量研究的。
Coupled behaviors refer to the activities of one to many actors who are associated with each other in terms of certain relationships. With increasing network and community-based events and applications, such as group-based crime and social network interactions, behavior coupling contributes to the causes of eventual business problems. Effective approaches for analyzing coupled behaviors are not available, since existing methods mainly focus on individual behavior analysis. This paper discusses the problem of Coupled Behavior Analysis (CBA) and its challenges. A Coupled Hidden Markov Model (CHMM)-based approach is illustrated to model and detect abnormal group-based trading behaviors. The CHMM models cater for: 1) multiple behaviors from a group of people, 2) behavioral properties, 3) interactions among behaviors, customers, and behavioral properties, and 4) significant changes between coupled behaviors. We demonstrate and evaluate the models on order-book-level stock tick data from a major Asian exchange and demonstrate that the proposed CHMMs outperforms HMM-only for modeling a single sequence or combining multiple single sequences, without considering coupling relationships to detect anomalies. Finally, we discuss interaction relationships and modes between coupled behaviors, which are worthy of substantial study.