In this paper, we present a method for exploratory data analysis of streaming data based on probabilistic graphical models (latent variable models). This method is illustrated by concept drift tracking, using financial client data from a European regional bank. For this particular setting, the analyzed data spans the period from April 2007 to March 2014 and therefore starts before the beginning of the financial crisis of 2008. The implied changes in the economic climate during this period manifests itself as concept drift in the underlying data generating distribution. We explore and analyze this financial client data using a probabilistic graphical modeling framework that provides an explicit representation of concept drift as an integral part of the model. We show how learning these types of models from data provides additional insight into the hidden mechanisms governing the drift in the domain. We present an iterative approach for identifying disparate factors that jointly account for the drift in the domain. This includes a semantic characterization of one of the main influencing drift factors. Based on the experiences and results obtained from analyzing the financial data, we discuss the applicability of the framework within a more general context.

中文翻译：

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分析概念漂移：金融领域的案例研究

在本文中，我们提出了一种基于概率图形模型（潜在变量模型）的流数据探索性数据分析方法。使用来自欧洲地区银行的金融客户数据，通过概念漂移跟踪来说明此方法。对于此特定设置，分析的数据跨度为2007年4月至2014年3月，因此在2008年金融危机开始之前开始。在此期间，经济气候的隐含变化体现为基础数据生成中的概念漂移分配。我们使用概率图形建模框架探索和分析该金融客户数据，该框架提供了概念漂移的明确表示，并将其作为模型的组成部分。我们展示了如何从数据中学习这些类型的模型如何提供对控制域中漂移的隐藏机制的更多了解。我们提出了一种迭代方法，用于确定共同解决域漂移的不同因素。这包括对主要影响漂移因素之一的语义描述。基于从分析财务数据中获得的经验和结果，我们在更一般的背景下讨论了该框架的适用性。