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Hybridization of analytic programming and differential evolution for time series prediction

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dc.title Hybridization of analytic programming and differential evolution for time series prediction en
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Viktorin, Adam
dc.contributor.author Pluháček, Michal
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Zelinka, Ivan
dc.relation.ispartof Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.identifier.issn 0302-9743 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-319-59650-1
dc.identifier.isbn 978-3-319-59649-5
dc.date.issued 2017
utb.relation.volume 10334 LNCS
dc.citation.spage 686
dc.citation.epage 698
dc.event.title 12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017
dc.event.location Logroño (La Rioja)
utb.event.state-en Spain
utb.event.state-cs Španělsko
dc.event.sdate 2017-06-21
dc.event.edate 2017-06-23
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-319-59650-1_58
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-59650-1_58
dc.subject Analytic programming en
dc.subject Differential evolution en
dc.subject SHADE en
dc.subject Time series prediction en
dc.description.abstract This research deals with the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series prediction. This paper provides a closer insight into applicability and performance of the hybrid connection between AP and different strategies of DE. AP can be considered as a powerful open framework for symbolic regression thanks to its usability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research, is to explore and investigate the applicability and differences in performance of AP driven by basic canonical strategy of DE as well as by the state of the art strategy, which is Success-History based Adaptive Differential Evolution (SHADE). An experiment with three case studies has been carried out here with the several time series consisting of GBP/USD exchange rate, where the first 2/3 of data were used for regression process and the last 1/3 of the data were used as a verification for prediction process. The differences between regression/prediction models synthesized by means of AP as a direct consequences of different DE strategies performances are briefly discussed within conclusion section of this paper. © Springer International Publishing AG 2017. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007263
utb.identifier.obdid 43877227
utb.identifier.scopus 2-s2.0-85021704649
utb.identifier.wok 000432880600058
utb.source d-scopus
dc.date.accessioned 2017-09-03T21:40:07Z
dc.date.available 2017-09-03T21:40:07Z
dc.description.sponsorship Grant Agency of the Czech Republic - GACR [P103/15/06700S]; Ministry of Education of the Czech Republic [MSMT-7778/2014]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Grant SGS of VSB-Technical University of Ostrava [2017/134]; Internal Grant Agency of Tomas Bata University [IGA/Cebia-Tech/2017/004]
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Kadavý, Tomáš
utb.fulltext.affiliation Roman Senkerik 1(&) , Adam Viktorin 1 , Michal Pluhacek 1 , Tomas Kadavy 1 , and Ivan Zelinka 2 1 Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {senkerik,aviktorin,pluhacek,kadavy}@fai.utb.cz 2 Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, 17. Listopadu 15, 708 33 Ostrava-Poruba, Czech Republic ivan.zelinka@vsb.cz
utb.fulltext.dates -
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utb.fulltext.sponsorship This work was supported by Grant Agency of the Czech Republic - GACR P103/15/06700S, further by project NPU I No. MSMT-7778/2014 by the Ministry of Education of the Czech Republic and also by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, partially supported by Grant SGS 2017/134 of VSB-Technical University of Ostrava; and by Internal Grant Agency of Tomas Bata University under the projects No. IGA/Cebia-Tech/2017/004.
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, Zlin, Czech Republic; Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, 17. Listopadu 15, Ostrava-Poruba, Czech Republic
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