Hybridization of analytic programming and differential evolution for time series prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F17%3A63517237" target="_blank" >RIV/70883521:28140/17:63517237 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/17:10238519 RIV/61989100:27740/17:10238519
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-59650-1_58" target="_blank" >http://dx.doi.org/10.1007/978-3-319-59650-1_58</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-59650-1_58" target="_blank" >10.1007/978-3-319-59650-1_58</a>
Alternative languages
Result language
angličtina
Original language name
Hybridization of analytic programming and differential evolution for time series prediction
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN
978-3-319-59649-5
ISSN
0302-9743
e-ISSN
neuvedeno
Number of pages
13
Pages from-to
686-698
Publisher name
Springer-Verlag Berlin
Place of publication
Heidelberg
Event location
La Rioja
Event date
Jun 21, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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