Analyzing concept drift: A case study in the financial sector-Reference-Cited by-同舟云学术

Analyzing concept drift: A case study in the financial sector

Published:2020-05-21 Issue:3 Volume:24 Page:665-688
ISSN:1088-467X
Container-title:Intelligent Data Analysis
language:
Short-container-title:IDA

Author:

Masegosa Andrés R.¹,Martínez Ana M.²,Ramos-López Darío³,Langseth Helge⁴,Nielsen Thomas D.⁵,Salmerón Antonio¹

Affiliation:

1. Department of Mathematics, University of Almería, Almería, Spain

2. Vestas Wind Systems A/S, København S, Denmark

3. Department of Applied Mathematics, Materials Science and Engineering, and Electronic Technology, Rey Juan Carlos University, Madrid, Spain

4. Department of Computer Science, The Norwegian University of Science and Technology, Trondheim, Norway

5. Department of Computer Science, Aalborg University, Aalborg, Denmark

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Theoretical Computer Science

Reference24 articles.

1. M.J. Beal, Variational algorithms for approximate Bayesian inference, PhD thesis, Gatsby Computational Neuroscience Unit, University College London, 2003.

2. MOA: Massive Online Analysis;Bifet;Journal of Machine Learning Research,2010

3. H. Borchani, A.M. Martínez, A. Masegosa, H. Langseth, T.D. Nielsen, A. Salmerón, A. Fernández, A.L. Madsen and R. Sáez, Modeling concept drift: a probabilistic graphical model based approach, In Proc. of The Fourteenth Int. Symposium on IDA, 2015, pp. 72–83.

4. Fieller stability measure: A novel model-dependent backtesting approach;Bravo;Journal of the Operational Research Society,2015

5. R. Cabañas, A.M. Martínez, A.R. Masegosa, D. Ramos-López, A. Samerón, T.D. Nielsen, H. Langseth and A.L. Madsen, Financial data analysis with pgms using amidst, In Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on Data Mining, IEEE, 2016, pp. 1284–1287.

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