Financial Data Analysis with PGMs Using AMIDST

The AMIDST Toolbox an open source Java 8 library for scalable learning of probabilistic graphical models (PGMs) based on both batch and streaming data. An important application domain with streaming data characteristics is the banking sector, where we may want to monitor individual customers (based on their financial situation and behavior) as well as the general economic climate. Using a real financial data set from a Spanish bank, we have previously proposed and demonstrated a novel PGM framework for performing this type of data analysis with particular focus on concept drift. The framework is implemented in the AMIDST Toolbox, which was also used to conduct the reported analyses. In this paper, we provide an overview of the toolbox and illustrate with code examples how the toolbox can be used for setting up and performing analyses of this particular type.

[1]  Uffe Kjærulff,et al.  Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis , 2007, Information Science and Statistics.

[2]  Andrés R. Masegosa,et al.  Parallel Importance Sampling in Conditional Linear Gaussian Networks , 2015, CAEPIA.

[3]  Andrés R. Masegosa,et al.  Modeling Concept Drift: A Probabilistic Graphical Model Based Approach , 2015, IDA.

[4]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[5]  Thomas Brox,et al.  Maximum Likelihood Estimation , 2019, Time Series Analysis.

[6]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[7]  Charles M. Bishop,et al.  Variational Message Passing , 2005, J. Mach. Learn. Res..

[8]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[9]  João Gama,et al.  A survey on concept drift adaptation , 2014, ACM Comput. Surv..

[10]  W. R. Shao,et al.  Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis , 2008 .