Ensemble of flexible neural tree and ordinary differential equations for small-time scale network traffic prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86092835" target="_blank" >RIV/61989100:27240/13:86092835 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.4304/jcp.8.12.3039-3046" target="_blank" >http://dx.doi.org/10.4304/jcp.8.12.3039-3046</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4304/jcp.8.12.3039-3046" target="_blank" >10.4304/jcp.8.12.3039-3046</a>
Alternative languages
Result language
angličtina
Original language name
Ensemble of flexible neural tree and ordinary differential equations for small-time scale network traffic prediction
Original language description
Accurate models play important roles in capturing the salient characteristics of the network traffic, analyzing and simulating for the network dynamic, and improving the predictive ability for system dynamics. In this study, the ensemble of the flexibleneural tree (FNT) and system models expressed by the ordinary differential equations (ODEs) is proposed to further improve the accuracy of time series forecasting. Firstly, the additive tree model is introduced to represent more precisely ODEs for the network dynamics. Secondly, the structures and parameters of FNT and the additive tree model are optimized based on the Genetic Programming (GP) and the Particle Swarm Optimization algorithm (PSO). Finally, the expected level of performance is verified byusing the proposed method, which provides a reliable forecast model for small-time scale network traffic. Experimental results reveal that the proposed method is able to estimate the small-time scale network traffic measurement data with
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Computers
ISSN
1796-203X
e-ISSN
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Volume of the periodical
8
Issue of the periodical within the volume
12
Country of publishing house
GB - UNITED KINGDOM
Number of pages
8
Pages from-to
3039-3046
UT code for WoS article
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EID of the result in the Scopus database
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