Financial Data Analysis with PGMs Using AMIDST

Rafael Cabanas, Ana M. Martinez, Andres R. Masegosa, Dario Ramos-Lopez, Antonio Sameron, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
Number of pages4
Publication date30 Jan 2017
Article number7836816
ISBN (Electronic)978-1-5090-5910-2
Publication statusPublished - 30 Jan 2017
Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
Duration: 12 Dec 201615 Dec 2016


Conference16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
SponsorIEEE Computer Society


  • Concept drift
  • Data streams
  • Financial data
  • Java 8
  • Latent variable models
  • Machine learning
  • Probabilistic graphical models
  • Scalable learning
  • Variational methods


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