Financial Data Analysis with PGMs Using AMIDST

Abstract

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.

DOI: 10.1109/ICDMW.2016.0185

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Cite this paper

@article{Cabaas2016FinancialDA, title={Financial Data Analysis with PGMs Using AMIDST}, author={Rafael Caba{\~n}as and Ana M. Mart{\'i}nez and Andr{\'e}s R. Masegosa and Dar{\'i}o Ramos-L{\'o}pez and Antonio Salmer{\'o}n and Thomas D. Nielsen and Helge Langseth and Anders L. Madsen}, journal={2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)}, year={2016}, pages={1284-1287} }