Exploiting heterogeneous parallelism on hybrid metaheuristics for vector autoregression models

Cuenca Muñoz, Antonio Javier; Cutillas Lozano, José Matías; Giménez, Domingo; Pérez Bernabeu, Alberto; López Espín, José J.

Publication:
Exploiting heterogeneous parallelism on hybrid metaheuristics for vector autoregression models

dc.contributor.author	Cuenca Muñoz, Antonio Javier
dc.contributor.author	Cutillas Lozano, José Matías
dc.contributor.author	Giménez, Domingo
dc.contributor.author	Pérez Bernabeu, Alberto
dc.contributor.author	López Espín, José J.
dc.contributor.department	Ingeniería y Tecnología de Computadores
dc.date.accessioned	2025-11-10T11:25:34Z
dc.date.available	2025-11-10T11:25:34Z
dc.date.copyright	© 2020 by the authors
dc.date.issued	2020-10-27
dc.description.abstract	In the last years, the huge amount of data available in many disciplines makes the mathematical modeling, and, more concretely, econometric models, a very important technique to explain those data. One of the most used of those econometric techniques is the Vector Autoregression Models (VAR) which are multi-equation models that linearly describe the interactions and behavior of a group of variables by using their past. Traditionally, Ordinary Least Squares and Maximum likelihood estimators have been used in the estimation of VAR models. These techniques are consistent and asymptotically efficient under ideal conditions of the data and the identification problem. Otherwise, these techniques would yield inconsistent parameter estimations. This paper considers the estimation of a VAR model by minimizing the difference between the dependent variables in a certain time, and the expression of their own past and the exogenous variables of the model (in this case denoted as VARX model). The solution of this optimization problem is approached through hybrid metaheuristics. The high computational cost due to the huge amount of data makes it necessary to exploit High-Performance Computing for the acceleration of methods to obtain the models. The parameterized, parallel implementation of the metaheuristics and the matrix formulation ease the simultaneous exploitation of parallelism for groups of hybrid metaheuristics. Multilevel and heterogeneous parallelism are exploited in multicore CPU plus multiGPU nodes, with the optimum combination of the different parallelism parameters depending on the particular metaheuristic and the problem it is applied to.
dc.format	application/pdf
dc.format.extent	18
dc.identifier.citation	Cuenca J, Cutillas-Lozano J-M, Giménez D, Pérez-Bernabeu A, López-Espín JJ. Exploiting Heterogeneous Parallelism on Hybrid Metaheuristics for Vector Autoregression Models. Electronics. 2020; Vol. 9 (11 ): 1781.
dc.identifier.doi	https://doi.org/10.1016/J.PROCS.2017.05.041
dc.identifier.eissn	2079-9292
dc.identifier.uri	http://hdl.handle.net/10201/171929
dc.language	eng
dc.publisher	MDPI
dc.relation	Este trabajo forma parte de los proyectos de investigación RTI2018-098156-B-C53 y TIN2016-80565-R. financiados por la Agencia Estatal de Investigación (AEI), el Ministerio de Ciencia e Innovación (MICINN) y Fondos FEDER de la Comisión Europea
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/pii/S1877050917305586?via%3Dihub
dc.rights	Attribution 4.0 International
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	Computational econometric
dc.subject	VAR models
dc.subject	Parallelism
dc.subject	Metaheuristics
dc.subject	GPU
dc.subject.ods	Objetivo 8: Crecimiento económico
dc.subject.ods	Objetivo 9: Infraestructura
dc.title	Exploiting heterogeneous parallelism on hybrid metaheuristics for vector autoregression models
dc.type	info:eu-repo/semantics/article
dspace.entity.type	Publication	es
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