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

doi:10.1109/ICDMW.2016.0185

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

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

4 Citationer (Scopus)

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.

Originalsprog	Engelsk
Titel	Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
Antal sider	4
Forlag	IEEE
Publikationsdato	30 jan. 2017
Sider	1284-1287
Artikelnummer	7836816
ISBN (Elektronisk)	978-1-5090-5910-2
DOI	https://doi.org/10.1109/ICDMW.2016.0185
Status	Udgivet - 30 jan. 2017
Begivenhed	16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spanien Varighed: 12 dec. 2016 → 15 dec. 2016

Konference

Konference	16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
Land/Område	Spanien
By	Barcelona
Periode	12/12/2016 → 15/12/2016
Sponsor	IEEE Computer Society

Adgang til dokumentet

10.1109/ICDMW.2016.0185

AUB Link

Søg efter materialet i Aalborg Universitetsbiblioteks søgemaskine

Andre filer og links

http://www.scopus.com/inward/record.url?scp=85015204902&partnerID=8YFLogxK

Citationsformater

@inproceedings{b6626d5e26414dc4ad73f86ae4c3714c,

title = "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.",

keywords = "Concept drift, Data streams, Financial data, Java 8, Latent variable models, Machine learning, Probabilistic graphical models, Scalable learning, Variational methods",

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

year = "2017",

month = jan,

day = "30",

doi = "10.1109/ICDMW.2016.0185",

language = "English",

pages = "1284--1287",

booktitle = "Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016",

publisher = "IEEE",

address = "United States",

note = "16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 ; Conference date: 12-12-2016 Through 15-12-2016",

}

Cabanas, R, Martinez, AM, Masegosa, AR, Ramos-Lopez, D, Sameron, A, Nielsen, TD, Langseth, H & Madsen, AL 2017, Financial Data Analysis with PGMs Using AMIDST. i Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016., 7836816, IEEE, s. 1284-1287, 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016, Barcelona, Spanien, 12/12/2016. https://doi.org/10.1109/ICDMW.2016.0185

TY - GEN

T1 - Financial Data Analysis with PGMs Using AMIDST

AU - Cabanas, Rafael

AU - Martinez, Ana M.

AU - Masegosa, Andres R.

AU - Ramos-Lopez, Dario

AU - Sameron, Antonio

AU - Nielsen, Thomas D.

AU - Langseth, Helge

AU - Madsen, Anders L.

PY - 2017/1/30

Y1 - 2017/1/30

N2 - 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.

AB - 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.

KW - Concept drift

KW - Data streams

KW - Financial data

KW - Java 8

KW - Latent variable models

KW - Machine learning

KW - Probabilistic graphical models

KW - Scalable learning

KW - Variational methods

UR - http://www.scopus.com/inward/record.url?scp=85015204902&partnerID=8YFLogxK

U2 - 10.1109/ICDMW.2016.0185

DO - 10.1109/ICDMW.2016.0185

M3 - Article in proceeding

SP - 1284

EP - 1287

BT - Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016

PB - IEEE

T2 - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016

Y2 - 12 December 2016 through 15 December 2016

ER -

Financial Data Analysis with PGMs Using AMIDST

Abstract

Konference

Adgang til dokumentet

AUB Link

Andre filer og links

Fingeraftryk

Citationsformater